ia-qa-toolbox

endpoint

https://www.ia-qa.com/mcp

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is ia-qa-toolbox live?

Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.

Is ia-qa-toolbox free to use?

Partly — some of its tools are open, others need a key or payment.

What tools does ia-qa-toolbox have?

153 tools: generate_test_cases, analyze_diff_bugs, diff_mappings, prompt_injection_scan, hallucination_check, guardrail_test, redact_pii, secret_scan, ….

Is ia-qa-toolbox safe to connect?

Caution — its card or tool descriptions carry text addressed to the agent reading them (forged-system-turn). Read them before connecting an agent.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• get_testing_guidelines reads open 3d ago

  Query the IA-QA methodology knowledge base. Returns structured testing guidelines, assertion strategies, thresholds, best practices, and relevant MCP tools for a given topic. Call without a topic to list all available topics. Topics: llm-unit-testing, rag-pipeline, prompt-stability, prompt-ab-testing, embedding-quality, eval-framework, semantic-testing, auto-testing, security, api-testing, ci-cd, multimodal, llm-data-security, agent-observability, pro-tips, learning-paths, golden-dataset, selector-drift, qa-recipes, playbooks. Not sure where to start testing an LLM, RAG pipeline or agent? Call without a topic (or with "start-here"): it maps what you are testing to the tools to call and the output field to gate CI on. A plain question such as "how do I test my RAG" also resolves to the right topic.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637

  {
    "type": "object",
    "examples": [
      {
        "topic": "rag-pipeline"
      }
    ],
    "properties": {
      "topic": {
        "enum": [
          "start-here",
          "llm-unit-testing",
          "rag-pipeline",
          "prompt-stability",
          "prompt-ab-testing",
          "embedding-quality",
          "eval-framework",
          "semantic-testing",
          "auto-testing",
          "security",
          "api-testing",
          "ci-cd",
          "multimodal",
          "llm-data-security",
          "agent-observability",
          "pro-tips",
          "learning-paths",
          "golden-dataset",
          "selector-drift",
          "qa-recipes",
          "playbooks"
        ],
        "type": "string",
        "description": "The testing topic to retrieve guidelines for. Omit to get the start-here map and the full list of available topics."
      }
    }
  }

  arguments 37 lines copy
• sandbox_scenario open 27h ago

  Get a ready-made selector-drift test case with a known-correct answer, for testing this MCP server or an agent workflow end to end. Each scenario is a real DOM capture of a deliberately breakable app, taken before and after one specific UI change (a renamed label, two swapped buttons, a duplicated locator, an element moved behind a menu…), plus the verdict those two contracts MUST produce. Call with no arguments to list the scenarios; call with a scenario id to get "baseline" and "current" mappings. THE LOOP: pass baseline and current to diff_mappings, then compare its "verdict" and "counts" to this tool's "expected" — they must match exactly. A mismatch means this server's diff engine has drifted, not that your inputs are wrong. Deterministic and offline: the captures are committed fixtures, identical on every call. Try it live at https://www.ia-qa.com/devtools/sandbox

  mcp-tool

  123456789101112131415161718

  {
    "type": "object",
    "examples": [
      {
        "scenario": "swap-label"
      }
    ],
    "properties": {
      "scenario": {
        "type": "string",
        "description": "Scenario id. Omit to list every available scenario with its expected verdict. Ids: no-change, swap-label, add-testid, duplicate-role-name, remove-element, insert-sibling, rename-label, counter-label, move-behind-menu, add-element"
      },
      "include_html": {
        "type": "boolean",
        "description": "Include the generated HTML of the mutated page (default false). Only useful if you want to render or re-capture it yourself; the loop does not need it."
      }
    }
  }

  arguments 18 lines copy
• generate_ci_workflow open 3d ago

  Generate a ready-to-commit GitHub Actions workflow that gates a build on IA-QA: "eval_contract" runs a .ia-eval.yaml through ia-qa-com/eval-action@v1 (needs a provider key as a repo secret), "cli_checks" runs deterministic tools via npx @ia-qa/cli and fails the build on their verdict, "selector_drift" gates E2E selectors with @ia-qa/self-healing. Deterministic template, no LLM. Returns the YAML, the secrets to create and the next steps. Pair with generate_eval_yaml for the contract.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980

  {
    "type": "object",
    "examples": [
      {}
    ],
    "properties": {
      "cron": {
        "type": "string",
        "description": "For schedule (default '0 6 * * 1')."
      },
      "gate": {
        "enum": [
          "eval_contract",
          "cli_checks",
          "both",
          "selector_drift",
          "all"
        ],
        "type": "string",
        "description": "Default eval_contract. both = CLI checks + eval; all = CLI checks, drift, eval."
      },
      "provider": {
        "enum": [
          "groq",
          "openai",
          "anthropic",
          "google"
        ],
        "type": "string",
        "description": "Decides which repo secret is wired (default groq)."
      },
      "triggers": {
        "type": "array",
        "items": {
          "enum": [
            "push",
            "pull_request",
            "workflow_dispatch",
            "schedule"
          ],
          "type": "string"
        },
        "description": "Default push + pull_request."
      },
      "cli_tools": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Tools to gate on, e.g. [\"secret_scan\",\"prompt_injection_scan\"]."
      },
      "min_score": {
        "type": "number",
        "description": "Override the contract min_score (0-100)."
      },
      "app_base_url": {
        "type": "string",
        "description": "[selector_drift] Default http://127.0.0.1:3000; must match .ia-qa/config.json."
      },
      "fail_on_fail": {
        "type": "boolean",
        "description": "Default true; false reports without gating."
      },
      "node_version": {
        "type": "string",
        "description": "Default \"20\"."
      },
      "contract_path": {
        "type": "string",
        "description": "Default evals/smoke.ia-eval.yaml."
      },
      "workflow_name": {
        "type": "string"
      },
      "app_start_command": {
        "type": "string",
        "description": "[selector_drift] Boots the app, e.g. \"npm run start:ci\". Never guessed: omitted, the step is a TODO."
      }
    }
  }

  arguments 80 lines copy
• list_local_tests open 3d ago

  Discover .ia-eval.yaml LLM test suite files in the project directory. Scans CWD and standard sub-directories (evals/, tests/, contracts/). Returns file paths ready to pass to run_eval_contract.

  mcp-tool

  123456789101112

  {
    "type": "object",
    "examples": [
      {}
    ],
    "properties": {
      "dir": {
        "type": "string",
        "description": "Directory to scan (defaults to server CWD)"
      }
    }
  }

  arguments 12 lines copy
• skill_interview open 2d ago

  Help a person write a SKILL.md they can test. No answers: the questions (why + example), written for the person; invented answers make the result meaningless. With answers: remarks per answer (no model), the SKILL.md, scenarios for test_skill, a starter .ia-eval.yaml.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253

  {
    "type": "object",
    "examples": [
      {}
    ],
    "properties": {
      "name": {
        "type": "string"
      },
      "answers": {
        "type": "object",
        "properties": {
          "bad": {
            "type": "string"
          },
          "job": {
            "type": "string"
          },
          "good": {
            "type": "string"
          },
          "input": {
            "type": "string"
          },
          "never": {
            "type": "string"
          },
          "steps": {
            "type": "string"
          },
          "format": {
            "type": "string"
          },
          "output": {
            "type": "string"
          },
          "context": {
            "type": "string"
          },
          "unclear": {
            "type": "string"
          },
          "boundary": {
            "type": "string"
          },
          "requests": {
            "type": "string"
          }
        },
        "description": "Keyed by question id; each step can be sent alone."
      }
    }
  }

  arguments 53 lines copy
• list_llm_models reads open 3d ago

  List all LLM models available on ia-qa.com with their provider, API endpoint, and capabilities. Filter by provider name (e.g. "Groq", "HuggingFace", "OpenAI") or return the full catalog. Use this to discover which models are available before calling an LLM API, or to compare providers.

  mcp-tool

  1234567891011121314

  {
    "type": "object",
    "examples": [
      {
        "provider": "groq"
      }
    ],
    "properties": {
      "provider": {
        "type": "string",
        "description": "Filter by provider name (case-insensitive). E.g. \"Groq\", \"HuggingFace\", \"OpenAI\", \"Anthropic\", \"Google\", \"DeepSeek\", \"xAI\", \"Ollama\". Omit for full catalog."
      }
    }
  }

  arguments 14 lines copy
• llm_fit_finder open 2d ago

  Shortlist LLMs on facts, no ranking: cloud models with the features you need within a monthly budget, cheapest first; or local models that fit your VRAM, largest first, with the ollama command. A shortlist is not a choice: only your own cases show which model fits.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849

  {
    "type": "object",
    "examples": [
      {
        "top_n": 5,
        "features": [
          "vision"
        ],
        "use_case": "chatbot"
      }
    ],
    "properties": {
      "mode": {
        "type": "string",
        "description": "cloud (API models) or local (Ollama/self-hosted). Default: cloud"
      },
      "top_n": {
        "type": "number",
        "description": "Number of models to return (default: 5)"
      },
      "vram_gb": {
        "type": "number",
        "description": "GPU VRAM in GB (only for mode=local). Default: 16"
      },
      "features": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Required features: vision, function_calling, json_mode, streaming, reasoning"
      },
      "use_case": {
        "type": "string",
        "description": "Only \"reasoning\" filters (requires the feature); chatbot | code | rag | summarization | classification | agents | multilingual rank nothing"
      },
      "max_budget": {
        "type": "number",
        "description": "Maximum monthly budget in USD (based on tokens_per_day)"
      },
      "quantization": {
        "type": "string",
        "description": "Quantization (only for mode=local): Q4_K_M | Q8_0 | FP16. Default: Q4_K_M"
      },
      "tokens_per_day": {
        "type": "number",
        "description": "Estimated daily token volume (default: 100000)"
      }
    }
  }

  arguments 49 lines copy
• timestamp_convert open 9h ago

  Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the current time. Returns iso, unix_s, unix_ms, utc, date, and time fields.

  mcp-tool

  12345678910111213

  {
    "type": "object",
    "examples": [
      {
        "input": 0
      }
    ],
    "properties": {
      "input": {
        "description": "Unix timestamp (number, seconds or ms) or ISO date string. Omit to get the current time."
      }
    }
  }

  arguments 13 lines copy
• fetch_veille_feed reads open 3d ago

  Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willison). Each article carries summary_source — the XML tag the summary was read from, or "none" when the feed publishes titles and links only; an empty summary with summary_source "none" is a property of that feed, not a parse failure.

  mcp-tool

  12345678910111213141516

  {
    "type": "object",
    "examples": [
      {}
    ],
    "properties": {
      "limit": {
        "type": "number",
        "description": "Max articles to return (default: 20, max: 50)"
      },
      "category": {
        "type": "string",
        "description": "Filter: \"qa\" (testing/quality), \"ai\" (AI/LLM/agents), \"all\" (default — both)"
      }
    }
  }

  arguments 16 lines copy
• lorem_ipsum reads open 2d ago

  Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20), and approximate words per sentence (3–30).

  mcp-tool

  1234567891011121314151617181920212223

  {
    "type": "object",
    "examples": [
      {
        "paragraphs": 3,
        "sentences_per_paragraph": 2
      }
    ],
    "properties": {
      "paragraphs": {
        "type": "number",
        "description": "Number of paragraphs to generate (1–10, default: 1)"
      },
      "words_per_sentence": {
        "type": "number",
        "description": "Approximate words per sentence (3–30, default: 10)"
      },
      "sentences_per_paragraph": {
        "type": "number",
        "description": "Sentences per paragraph (1–20, default: 5)"
      }
    }
  }

  arguments 23 lines copy
• generate_uuid open 3d ago

  Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any scenario requiring a guaranteed-unique string. Returns up to 100 UUIDs in one call.

  mcp-tool

  1234567891011121314

  {
    "type": "object",
    "examples": [
      {
        "count": 5
      }
    ],
    "properties": {
      "count": {
        "type": "number",
        "description": "Number of UUIDs to generate (1–100, default: 1)"
      }
    }
  }

  arguments 14 lines copy
• similarity_score open 20h ago

  Compute text similarity between reference and hypothesis using multiple metrics: Cosine (BoW, TF-IDF), Jaccard, ROUGE-1, ROUGE-2, ROUGE-L, and BLEU. No API key needed. Ideal for LLM eval (expected vs actual), RAG quality checks, and NLG benchmarking. Supports batch mode.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950

  {
    "type": "object",
    "examples": [
      {
        "reference": "the cat sat on the mat",
        "threshold": 0.5,
        "hypothesis": "the cat sat on the mat"
      }
    ],
    "properties": {
      "batch": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "reference",
            "hypothesis"
          ],
          "properties": {
            "reference": {
              "type": "string"
            },
            "hypothesis": {
              "type": "string"
            }
          }
        },
        "description": "Batch mode: array of {reference, hypothesis} pairs."
      },
      "metrics": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Metrics to compute (default: all). Options: \"cosine_bow\", \"cosine_tfidf\", \"jaccard\", \"rouge1\", \"rouge2\", \"rougeL\", \"bleu\""
      },
      "reference": {
        "type": "string",
        "description": "Reference / expected text (ground truth)"
      },
      "threshold": {
        "type": "number",
        "description": "Optional pass/fail threshold (0-1). Applies to ROUGE-L F1 score."
      },
      "hypothesis": {
        "type": "string",
        "description": "Hypothesis / actual text (LLM output)"
      }
    }
  }

  arguments 50 lines copy
• multimodal_eval_guide open 2d ago

  Multimodal evaluation: action=guide (default) interprets a CLIP/FID/VQA score against reference thresholds; clip_score, fid_score, vqa_accuracy compute the metric (HuggingFace, or your VLM key for VQA); pipeline runs all three.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110

  {
    "type": "object",
    "examples": [
      {
        "score": 72,
        "metric": "clip_score"
      }
    ],
    "properties": {
      "fid": {
        "type": "object",
        "description": "[pipeline] {real_images, generated_images}",
        "additionalProperties": true
      },
      "vqa": {
        "type": "object",
        "description": "[pipeline] Same inputs as vqa_accuracy.",
        "additionalProperties": true
      },
      "clip": {
        "type": "object",
        "description": "[pipeline] {image_url, text}",
        "additionalProperties": true
      },
      "text": {
        "type": "string",
        "description": "[clip_score] Text to compare with the image."
      },
      "model": {
        "type": "string",
        "description": "[vqa_accuracy] Default gpt-4o."
      },
      "score": {
        "type": "number",
        "description": "[guide] A score to interpret."
      },
      "action": {
        "enum": [
          "guide",
          "clip_score",
          "fid_score",
          "vqa_accuracy",
          "pipeline"
        ],
        "type": "string"
      },
      "metric": {
        "enum": [
          "clip_score",
          "fid",
          "vqa_accuracy",
          "all"
        ],
        "type": "string",
        "description": "[guide]"
      },
      "api_key": {
        "type": "string",
        "description": "[vqa_accuracy] Key for the model's provider."
      },
      "image_url": {
        "type": "string",
        "description": "[clip_score, vqa_accuracy] Public image URL."
      },
      "test_cases": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "question": {
              "type": "string"
            },
            "accepted_answers": {
              "type": "array",
              "items": {
                "type": "string"
              }
            }
          }
        },
        "description": "[vqa_accuracy]"
      },
      "real_images": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "[fid_score] URLs."
      },
      "image_base64": {
        "type": "string",
        "description": "[clip_score, vqa_accuracy]"
      },
      "system_prompt": {
        "type": "string",
        "description": "[vqa_accuracy]"
      },
      "image_mime_type": {
        "type": "string",
        "description": "[clip_score, vqa_accuracy]"
      },
      "generated_images": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "[fid_score] URLs."
      }
    }
  }

  arguments 110 lines copy
• identify_caller open 3d ago

  Returns what the server knows about the current MCP client: clientInfo captured during initialize, User-Agent, and any _meta fields sent with this request. Useful for debugging caller identification.

  mcp-tool

  1234567891011121314151617181920212223

  {
    "type": "object",
    "examples": [
      {}
    ],
    "properties": {
      "_meta": {
        "type": "object",
        "properties": {
          "agent": {
            "type": "string"
          },
          "model": {
            "type": "string"
          },
          "version": {
            "type": "string"
          }
        },
        "description": "Optional self-identification. Keys: agent (string), model (string), version (string)."
      }
    }
  }

  arguments 23 lines copy
• jira_to_test_suite auth-required 31h ago

  Turn a Jira ticket into a test suite: Gherkin scenarios, E2E steps, API tests, test data and the ambiguities to clarify. Pass the issue object, or Jira credentials to fetch it. It never invents: a thin ticket returns an empty suite with verdict INCOMPLETE_SPEC and the questions to ask. test_suite._gherkin_warnings is a deterministic syntax check. BYOK: your LLM key.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859

  {
    "type": "object",
    "examples": [
      {
        "issue": {
          "key": "PROJ-1",
          "summary": "Password reset by email",
          "description": "A registered user can request a reset link; the link expires after 30 minutes."
        },
        "model": "gpt-4o-mini",
        "api_key": "<your-api-key>"
      }
    ],
    "required": [
      "api_key",
      "model"
    ],
    "properties": {
      "issue": {
        "type": "object",
        "description": "The ticket: key, summary, description, and optionally status, issue_type, priority, labels, comments (fetch_jira_issue returns this shape).",
        "additionalProperties": true
      },
      "model": {
        "type": "string",
        "description": "e.g. \"gpt-4o-mini\", \"claude-haiku-4-5-20251001\", \"gemini-3.5-flash\"."
      },
      "api_key": {
        "type": "string",
        "description": "Your LLM provider key."
      },
      "issue_key": {
        "type": "string",
        "description": "e.g. \"PROJ-123\", to fetch with the jira_* fields."
      },
      "jira_email": {
        "type": "string"
      },
      "jira_token": {
        "type": "string"
      },
      "max_tokens": {
        "type": "integer",
        "default": 8192,
        "description": "Default 8192."
      },
      "jira_base_url": {
        "type": "string"
      },
      "confluence_pages": {
        "type": "array",
        "items": {
          "type": "object",
          "additionalProperties": true
        },
        "description": "Pages from fetch_confluence_page, as documentation."
      }
    }
  }

  arguments 59 lines copy
• fix_gherkin auth-required 47h ago

  Fix Gherkin syntax warnings from a jira_to_test_suite result. Takes the current gherkin text and the _gherkin_warnings array, calls your LLM to fix ONLY the flagged issues (adds missing Given/When/Then steps, etc.), and returns the corrected Gherkin. Lightweight — uses ~300-500 tokens vs ~5k for a full regeneration. Requires BYOK LLM key.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940

  {
    "type": "object",
    "examples": [
      {
        "model": "gpt-4o-mini",
        "api_key": "<your-api-key>",
        "gherkin": "Feature: Login\n  Scenario: ok\n    given I open the page\n    Then I see the form",
        "warnings": [
          "Step keyword \"given\" should be capitalised"
        ]
      }
    ],
    "required": [
      "gherkin",
      "warnings",
      "api_key",
      "model"
    ],
    "properties": {
      "model": {
        "type": "string",
        "description": "LLM model to use for the fix, e.g. \"gpt-4o-mini\". Must belong to the provider whose key you passed in api_key."
      },
      "api_key": {
        "type": "string",
        "description": "Your own LLM provider API key (BYOK) — OpenAI \"sk-…\", Anthropic \"sk-ant-…\", Google \"AIzaSy…\", or Groq \"gsk_…\". There is no server-side key for this tool: if you do not have one, do not call it and do not invent a value — placeholders like \"configured\", \"your_api_key\" or a masked \"sk-…***…\" are rejected. Used for this call only, never stored."
      },
      "gherkin": {
        "type": "string",
        "description": "The current Gherkin text from the jira_to_test_suite result (test_suite.gherkin)."
      },
      "warnings": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "The _gherkin_warnings array from the jira_to_test_suite result."
      }
    }
  }

  arguments 40 lines copy
• generate_hmac reads auth-required 41h ago

  Compute an HMAC signature for a message using a secret key. Supports SHA-256 (default), SHA-512, SHA-1, and MD5. Used for API request signing, webhook verification (GitHub, Stripe, Twilio), and JWT validation.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637

  {
    "type": "object",
    "examples": [
      {
        "secret": "k",
        "message": "msg",
        "algorithm": "sha256"
      }
    ],
    "required": [
      "message",
      "secret"
    ],
    "properties": {
      "secret": {
        "type": "string",
        "description": "Secret key"
      },
      "message": {
        "type": "string",
        "description": "Message to sign"
      },
      "encoding": {
        "enum": [
          "hex",
          "base64",
          "base64url"
        ],
        "type": "string",
        "description": "Output encoding (default: hex)"
      },
      "algorithm": {
        "type": "string",
        "description": "Hash algorithm: sha256 (default), sha512, sha1, md5"
      }
    }
  }

  arguments 37 lines copy
• decode_jwt auth-required 2d ago

  Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry date. Use to inspect claims (sub, iss, exp, roles) during debugging or when integrating with an auth provider.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "token": "eyJhbGciOiJIUzI1NiJ9.eyJleHAiOjQxMDI0NDQ4MDB9.sig"
      }
    ],
    "required": [
      "token"
    ],
    "properties": {
      "token": {
        "type": "string",
        "description": "The JWT string to decode (header.payload.signature)"
      }
    }
  }

  arguments 17 lines copy
• generate_test_cases unknown 34h ago

  Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bound ("[8-64]", "min 8 chars", "at least 8 characters" — read from inputs, and from the feature prose when a sentence names exactly one field) yields the last accepted value AND the first rejected one; a format (email/url/uuid, from the type, the field name or the wording) yields malformed-value cases. A bound nobody declared is labelled as this tool's assumption, not as expected behaviour. gherkinFormat renders every case (cap 200, stated in the output) and gherkinScenarioCount lets you check it against totalCases.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "feature": "Login form with email and password"
      }
    ],
    "required": [
      "feature"
    ],
    "properties": {
      "inputs": {
        "type": "string",
        "description": "Optional: list of input parameters (one per line, e.g. \"email: string [required]\", \"password: string [required, min 8 chars]\", \"age: number [18-99]\")"
      },
      "feature": {
        "type": "string",
        "description": "Feature or function to test. Be specific: describe inputs, expected behaviour, context. Constraints stated here (\"password must be at least 8 characters\") are used when the sentence names exactly one field."
      }
    }
  }

  arguments 21 lines copy
• analyze_diff_bugs unknown 3d ago

  Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolation, eval/new Function, empty catch blocks, regex built from a variable, fewer catch blocks than before, and named authorization guards that disappeared. Every finding cites the line that produced it. It does NOT do data-flow analysis: it cannot follow a value to a sink, across functions or files, and an empty result is not a safety verdict (the response lists what it did not analyse). Advisory triage — use a static analyser for a real security gate.

  mcp-tool

  1234567891011121314151617181920212223242526

  {
    "type": "object",
    "examples": [
      {
        "version1": "if (amount > 0) charge(amount);",
        "version2": "if (amount >= 0) charge(amount);"
      }
    ],
    "required": [
      "version2"
    ],
    "properties": {
      "context": {
        "type": "string",
        "description": "Optional PR title or feature context for better analysis"
      },
      "version1": {
        "type": "string",
        "description": "Original code (before changes). If omitted, only the new version is analysed."
      },
      "version2": {
        "type": "string",
        "description": "New/modified code (after changes)"
      }
    }
  }

  arguments 26 lines copy
• diff_mappings unknown 2d ago

  Diff a baseline page mapping against a current one and return a CI-style verdict: PASS / FIX / BLOCK, plus per-element drift (ok, renamed, healable, ambiguous, lost, added, rebound). Pure and deterministic — provide two mappings as JSON with "elements" arrays of {role, name, selector, context?}. Use the companion @ia-qa/self-healing package (npm install -g @ia-qa/self-healing) to capture mappings from your app via its local MCP server ia-qa-heal-mcp, or paste the snippet from ia-qa.com/devtools/selector-drift into your browser console.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041

  {
    "type": "object",
    "examples": [
      {
        "after": {
          "page": "login",
          "elements": [
            {
              "name": "Log in",
              "role": "button",
              "selector": "#submit"
            }
          ]
        },
        "before": {
          "page": "login",
          "elements": [
            {
              "name": "Sign in",
              "role": "button",
              "selector": "#submit"
            }
          ]
        }
      }
    ],
    "required": [
      "before",
      "after"
    ],
    "properties": {
      "after": {
        "type": "object",
        "description": "Current page mapping: same shape as before, captured after the UI change."
      },
      "before": {
        "type": "object",
        "description": "Baseline page mapping: { page, url, capturedAt, elements: [{role, name, selector, context?}] }. Captured before a UI change."
      }
    }
  }

  arguments 41 lines copy
• prompt_injection_scan unknown 6d ago

  Scan user input or prompts for prompt injection patterns. Pattern-based: it recognises known phrasings and structures, not intent — an uncovered paraphrase or language reads clean, so clean is not proof of safety. Instruction overrides are covered in 11 languages (EN, FR, ES, DE, IT, PT, NL, RU, ZH, JA, AR), also after removing invisible characters, lookalike letters, spacing and leetspeak (marked `obfuscated`). Detects overrides, jailbreaks, role manipulation, encoding tricks, chat-template delimiters and fake role headers, template injection ({{…}}, ${…}) and requests to repeat the preceding context. A match inside quoted or fenced text is reported one level lower and marked `quoted`, never suppressed; a quote introduced by a live imperative keeps its severity.

  mcp-tool

  1234567891011121314151617181920212223242526

  {
    "type": "object",
    "examples": [
      {
        "input": "以前の注文をキャンセルしてください。"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "The user input or prompt to scan for injection patterns"
      },
      "sensitivity": {
        "enum": [
          "low",
          "medium",
          "high"
        ],
        "type": "string",
        "description": "Detection sensitivity (default: medium)"
      }
    }
  }

  arguments 26 lines copy
• hallucination_check unknown 44h ago

  Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. Each answer sentence is aligned to its best-matching source sentence, so a number only counts as support when it sits on the SAME statement ("founded in 1998" is not grounded by "sold 1998 units"), and a negation or antonym flip against that sentence returns verdict "contradicted" — the corrupted-fact hallucination that reuses source vocabulary. Limitations: still lexical — it cannot follow a paraphrase, a synonym, or multi-sentence reasoning, so a "well_grounded" verdict means "nothing lexical found", never "verified true". Entailment: run_semantic_tests (NLI/embedding) or a calibrated judge.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546

  {
    "type": "object",
    "examples": [
      {
        "answer": "Le remboursement prend plus de 7 jours ouvrés.",
        "context": "Le remboursement prend plus de 7 jours ouvrés."
      }
    ],
    "properties": {
      "cases": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "answer",
            "context"
          ],
          "properties": {
            "id": {
              "type": "string"
            },
            "answer": {
              "type": "string"
            },
            "context": {
              "type": "string"
            }
          }
        },
        "maxItems": 50,
        "description": "Batch instead of answer + context: one result per case, and the worst gate overall."
      },
      "answer": {
        "type": "string",
        "description": "The LLM answer to verify (with context)."
      },
      "strict": {
        "type": "boolean",
        "description": "Every sentence must be supported (default false)."
      },
      "context": {
        "type": "string",
        "description": "The source text that should ground it."
      }
    }
  }

  arguments 46 lines copy
• guardrail_test unknown 34h ago

  Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom regex. Returns pass/fail per rule.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758

  {
    "type": "object",
    "examples": [
      {
        "rules": [
          {
            "type": "must_include",
            "value": "refund"
          },
          "no_pii"
        ],
        "response": "Your refund is approved."
      }
    ],
    "required": [
      "response",
      "rules"
    ],
    "properties": {
      "rules": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "type": {
              "enum": [
                "must_include",
                "must_not_include",
                "max_length",
                "min_length",
                "format",
                "regex_match",
                "regex_not_match",
                "starts_with",
                "ends_with",
                "word_count_max",
                "word_count_min"
              ],
              "type": "string"
            },
            "label": {
              "type": "string",
              "description": "Optional human-readable label"
            },
            "value": {
              "type": "string",
              "description": "Value for the rule (text, number as string, regex, or format name)"
            }
          }
        },
        "description": "Array of guardrail rules to check"
      },
      "response": {
        "type": "string",
        "description": "The LLM response to test"
      }
    }
  }

  arguments 58 lines copy
• redact_pii unknown 6d ago

  Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, IBANs, credit cards, IP addresses, and JWT tokens with [REDACTED_TYPE] placeholders. Safe to use before logging or sending to an LLM.

  mcp-tool

  12345678910111213141516171819202122232425

  {
    "type": "object",
    "examples": [
      {
        "input": "order #12345678"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to redact PII from"
      },
      "types": {
        "type": "string",
        "description": "Comma-separated types to redact (default: all). Options: email, phone, ssn, iban, credit_card, ip_address, jwt"
      },
      "marker": {
        "type": "string",
        "description": "Custom replacement marker (default: \"REDACTED\"). Result: [REDACTED_EMAIL]"
      }
    }
  }

  arguments 25 lines copy
• secret_scan unknown never probed

  Scan text or code for leaked secrets: API keys (AWS, GCP, Azure, OpenAI, Anthropic, Stripe, GitHub, GitLab, Slack, Twilio, SendGrid, HuggingFace), Slack / Teams / Discord webhook URLs, private keys, JWTs, database connection strings, Bearer and Basic auth headers, and hard-coded values assigned to password/secret/token names (a value read at run time — Cypress.env(), os.getenv() — is not flagged). Returns findings with type, severity, line and a redacted preview. Regex-based, no network.

  mcp-tool

  123456789101112131415161718192021222324

  {
    "type": "object",
    "examples": [
      {
        "input": "redis://cache:6379"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": [
          "string",
          "array"
        ],
        "description": "Text to scan — or an array of texts / {id, text} for a batch (one result each, worst gate on top)."
      },
      "types": {
        "type": "string",
        "description": "Comma-separated families (default all): aws, gcp, azure, openai, anthropic, huggingface, github, gitlab, stripe, slack, teams, discord, cookie, twilio, sendgrid, jwt, private_key, connection_string, bearer, basic_auth, generic, or pattern names. An unknown value is rejected, never a silent \"clean\"."
      }
    }
  }

  arguments 24 lines copy
• generate_eval_yaml unknown 41h ago

  Generate a complete .ia-eval.yaml evaluation contract from a plain-language description of what your LLM should do. Uses Groq openai/gpt-oss-20b (server-side, no API key needed). Returns ready-to-run YAML for the LLM Test Runner (run_eval_contract). Picks appropriate evaluators (cosine_similarity, contains_check, hallucination_check, etc.) based on the task type.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142

  {
    "type": "object",
    "examples": [
      {
        "description": "Summarize a customer support ticket in one sentence."
      }
    ],
    "required": [
      "description"
    ],
    "properties": {
      "task_type": {
        "enum": [
          "rag",
          "summarization",
          "classification",
          "safety",
          "customer_support",
          "code_gen"
        ],
        "type": "string",
        "description": "Optional task type hint to guide evaluator selection."
      },
      "description": {
        "type": "string",
        "description": "Plain-language description of what the LLM under test should do. Be specific: describe inputs, expected behaviour, and constraints."
      },
      "system_prompt": {
        "type": "string",
        "description": "Optional system prompt of the LLM under test. Helps generate more accurate test cases."
      },
      "scenario_count": {
        "enum": [
          3,
          5,
          8
        ],
        "type": "number",
        "description": "Number of scenarios to generate (default: 5). Covers happy path + edge cases + adversarial."
      }
    }
  }

  arguments 42 lines copy
• validate_agent_trajectory unknown 4d ago

  Run declarative assertions on an agent trace (OpenAI tool calls, Anthropic tool_use, LangChain run trees, ReAct logs). Deterministic, no LLM. Assertions: order (tool A before B), must_call, must_not_call, max/min calls, no_error, recovery (a successful step after the last error), no_injection, no_pii. A step is errored when the trace marks it (is_error, status error/failed, success:false) or its text starts with Error:/Traceback — prose is never keyword-scanned. Verdict PASS / FAIL / INCONCLUSIVE (0 steps parsed, never PASS).

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128

  {
    "type": "object",
    "examples": [
      {
        "trace": [
          {
            "role": "assistant",
            "tool_calls": [
              {
                "function": {
                  "name": "search",
                  "arguments": "{}"
                }
              }
            ]
          }
        ],
        "assertions": [
          {
            "tool": "search",
            "type": "must_call"
          }
        ]
      }
    ],
    "required": [
      "trace",
      "assertions"
    ],
    "properties": {
      "trace": {
        "oneOf": [
          {
            "type": "string"
          },
          {
            "type": "object",
            "additionalProperties": true
          },
          {
            "type": "array",
            "items": {}
          }
        ],
        "description": "The trace: JSON (messages array, run tree) or a Thought/Action/Observation text log."
      },
      "format": {
        "enum": [
          "auto",
          "openai",
          "langchain"
        ],
        "type": "string",
        "description": "Default auto."
      },
      "assertions": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "type"
          ],
          "properties": {
            "id": {
              "type": "string"
            },
            "max": {
              "type": "number",
              "description": "[max_calls]"
            },
            "min": {
              "type": "number",
              "description": "[min_calls]"
            },
            "tool": {
              "type": "string",
              "description": "For must_call, must_not_call, max_calls, min_calls."
            },
            "type": {
              "enum": [
                "order",
                "must_call",
                "must_not_call",
                "max_calls",
                "min_calls",
                "no_error",
                "recovery",
                "no_injection",
                "no_pii"
              ],
              "type": "string",
              "description": "no_injection scans tool results (indirect injection); no_pii scans the final answer. Both report types and counts, never values."
            },
            "after": {
              "type": "string",
              "description": "[order] Called after."
            },
            "types": {
              "type": "string",
              "description": "[no_pii] Comma-separated: email, phone, ssn, credit_card, ip_address, jwt. Default all."
            },
            "where": {
              "enum": [
                "tool_results",
                "answer"
              ],
              "type": "string",
              "description": "[no_injection, no_pii] Override what is scanned."
            },
            "before": {
              "type": "string",
              "description": "[order] Called first."
            },
            "sensitivity": {
              "enum": [
                "low",
                "medium",
                "high"
              ],
              "type": "string",
              "description": "[no_injection] Default low (critical patterns only); higher also fires on ordinary text."
            }
          }
        },
        "maxItems": 30
      }
    }
  }

  arguments 128 lines copy
• test_skill unknown 5d ago

  Test a SKILL.md. No model, every call: lint (gate on lint.valid) and review (description lacks what/when, words shared with other_skills, leftovers, no done-check, missing files; why + fix each). Unless lint_only, a model picks a skill per request from names+descriptions: trigger_accuracy PASS ≥80, FIX ≥50, else BLOCK; your scenarios = gate, generated = signal; unanswered = INCONCLUSIVE. Server key 10 runs/day.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586

  {
    "type": "object",
    "examples": [
      {
        "skill_md": "---\nname: json-formatter\ndescription: Pretty-print a JSON string the user pastes.\n---\nFormat the JSON with 2-space indentation."
      }
    ],
    "required": [
      "skill_md"
    ],
    "properties": {
      "files": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Paths in its folder."
      },
      "model": {
        "type": "string",
        "description": "Default openai/gpt-oss-20b."
      },
      "api_key": {
        "type": "string",
        "description": "Used once, not stored."
      },
      "dir_name": {
        "type": "string",
        "description": "Its folder."
      },
      "skill_md": {
        "type": "string",
        "description": "Full SKILL.md, frontmatter included."
      },
      "lint_only": {
        "type": "boolean"
      },
      "scenarios": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "prompt",
            "should_trigger"
          ],
          "properties": {
            "prompt": {
              "type": "string"
            },
            "should_trigger": {
              "type": "boolean"
            }
          }
        },
        "description": "Max 20, near misses included."
      },
      "other_skills": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "name",
            "description"
          ],
          "properties": {
            "name": {
              "type": "string"
            },
            "description": {
              "type": "string"
            }
          }
        },
        "description": "Installed next to it."
      },
      "scenario_count": {
        "enum": [
          4,
          6,
          8,
          10
        ],
        "type": "number"
      }
    }
  }

  arguments 86 lines copy
• validate_mcp_response unknown 4d ago

  Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSON result and a set of checks to perform.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152

  {
    "type": "object",
    "examples": [
      {
        "response": "{\"user\":{\"name\":\"x\"}}",
        "forbidden_keys": "password"
      }
    ],
    "required": [
      "response"
    ],
    "properties": {
      "response": {
        "type": "string",
        "description": "The MCP tool result as a JSON string to validate"
      },
      "min_items": {
        "type": "number",
        "description": "If response is an array, minimum number of items expected"
      },
      "expected_type": {
        "enum": [
          "object",
          "array",
          "string",
          "number"
        ],
        "type": "string",
        "description": "Expected top-level type: \"object\", \"array\", \"string\", \"number\""
      },
      "required_keys": {
        "type": "string",
        "description": "Comma-separated list of keys that MUST exist in the response (dot-notation for nested: \"data.id, data.name\")"
      },
      "actual_latency": {
        "type": "number",
        "description": "Actual measured latency in ms (from the call)"
      },
      "forbidden_keys": {
        "type": "string",
        "description": "Comma-separated list of keys that MUST NOT exist (e.g. \"password, secret, token\")"
      },
      "max_size_bytes": {
        "type": "number",
        "description": "Maximum acceptable response size in bytes"
      },
      "max_response_ms": {
        "type": "number",
        "description": "Maximum acceptable latency in ms (will be compared if provided)"
      }
    }
  }

  arguments 52 lines copy
• llm_output_validator unknown never probed

  Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language detection, and safety patterns. Essential for QA testing LLM-powered features.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859

  {
    "type": "object",
    "examples": [
      {
        "output": "{\"a\":1}",
        "must_include": "a",
        "expected_format": "json",
        "must_not_include": "TODO"
      }
    ],
    "required": [
      "output"
    ],
    "properties": {
      "output": {
        "type": "string",
        "description": "The LLM output text to validate"
      },
      "max_length": {
        "type": "number",
        "description": "Maximum character length for the output"
      },
      "min_length": {
        "type": "number",
        "description": "Minimum character length for the output"
      },
      "check_safety": {
        "type": "boolean",
        "description": "Check for PII patterns (emails, phones, SSN), profanity signals, and prompt leakage"
      },
      "must_include": {
        "type": "string",
        "description": "Comma-separated strings that MUST appear in the output"
      },
      "expected_format": {
        "enum": [
          "json",
          "markdown",
          "code",
          "plain",
          "any"
        ],
        "type": "string",
        "description": "Expected output format"
      },
      "must_not_include": {
        "type": "string",
        "description": "Comma-separated strings that must NOT appear (e.g. \"TODO, FIXME, undefined, NaN\")"
      },
      "check_json_schema": {
        "type": "string",
        "description": "If expected_format is JSON, provide required keys as comma-separated list to validate the structure"
      },
      "expected_language": {
        "type": "string",
        "description": "Expected language of the output (en, fr, es, de…). Checks for common words."
      }
    }
  }

  arguments 59 lines copy
• compare_responses unknown 3d ago

  Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, length/structure deltas, JSON diff) and a verdict. If a `reference` (ground truth) is given, scores each output against it and picks the closer one. If `model` + `api_key` are given, an LLM judge also picks a qualitative winner for the task. No re-execution — you bring the outputs.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051

  {
    "type": "object",
    "examples": [
      {
        "response_a": "Paris is the capital.",
        "response_b": "Paris is the capital."
      }
    ],
    "required": [
      "response_a",
      "response_b"
    ],
    "properties": {
      "task": {
        "type": "string",
        "description": "The task/prompt both outputs were answering — used by the LLM judge for context"
      },
      "model": {
        "type": "string",
        "description": "Optional judge model id (BYOK). When set with api_key, an LLM judge picks a qualitative winner."
      },
      "api_key": {
        "type": "string",
        "description": "Optional API key for the judge model (BYOK). Used only for the judge call; never stored."
      },
      "label_a": {
        "type": "string",
        "description": "Label for output A (e.g. \"GPT-4o\", \"v1.0\")"
      },
      "label_b": {
        "type": "string",
        "description": "Label for output B (e.g. \"GPT-5-nano\", \"v1.1\")"
      },
      "reference": {
        "type": "string",
        "description": "Optional ground-truth / expected answer. If set, each output is scored against it and the closer one wins (deterministic)."
      },
      "check_json": {
        "type": "boolean",
        "description": "Try to parse as JSON and compare structurally (keys, types, values)"
      },
      "response_a": {
        "type": "string",
        "description": "First output (e.g. model A's answer)"
      },
      "response_b": {
        "type": "string",
        "description": "Second output (e.g. model B's answer)"
      }
    }
  }

  arguments 51 lines copy
• analyze_responses unknown 3d ago

  Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pairwise cosine agreement, the most-representative output, and the outlier. With a `reference` (ground truth): also ranks every output by closeness (token cosine + ROUGE-L composite) and names the closest. No LLM, no key, runs locally. The reference mode is gate-able in CI; the consensus/outlier mode is a signal (no reference to fail against). You bring the outputs (2+). A 2-way head-to-head with structural JSON diff is compare_responses.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748

  {
    "type": "object",
    "examples": [
      {
        "reference": "Paris is the capital of France.",
        "responses": [
          {
            "text": "Lyon is the capital.",
            "label": "A"
          },
          {
            "text": "Paris is the capital of France.",
            "label": "B"
          }
        ]
      }
    ],
    "required": [
      "responses"
    ],
    "properties": {
      "reference": {
        "type": "string",
        "description": "Optional ground-truth answer. If set, each output is also ranked by closeness to it and the closest one is named."
      },
      "responses": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "text"
          ],
          "properties": {
            "text": {
              "type": "string",
              "description": "The produced output"
            },
            "label": {
              "type": "string",
              "description": "Human name for this candidate (e.g. model id)"
            }
          }
        },
        "minItems": 2,
        "description": "The outputs to analyze (same task, N models/prompts/versions). Each item is a plain string or { \"label\": \"GPT-4o\", \"text\": \"...\" }. At least 2 required."
      }
    }
  }

  arguments 48 lines copy
• prompt_test_suite unknown 6d ago

  Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as input for manual or automated LLM evaluation.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162

  {
    "type": "object",
    "examples": [
      {
        "user_prompt": "Say hi",
        "system_prompt": "You are a helpful assistant."
      }
    ],
    "required": [
      "system_prompt",
      "user_prompt"
    ],
    "properties": {
      "max_tokens": {
        "type": "number",
        "description": "Max token budget for the test"
      },
      "temperature": {
        "type": "number",
        "description": "Temperature to use"
      },
      "user_prompt": {
        "type": "string",
        "description": "The user prompt to send"
      },
      "check_safety": {
        "type": "boolean",
        "description": "Include safety/PII checks in the rubric"
      },
      "must_include": {
        "type": "string",
        "description": "Required content (comma-separated)"
      },
      "system_prompt": {
        "type": "string",
        "description": "The system prompt under test"
      },
      "expected_format": {
        "enum": [
          "json",
          "markdown",
          "code",
          "plain",
          "any"
        ],
        "type": "string",
        "description": "Expected output format"
      },
      "must_not_include": {
        "type": "string",
        "description": "Forbidden content (comma-separated)"
      },
      "expected_behavior": {
        "type": "string",
        "description": "Description of what the LLM should do (free text)"
      },
      "adversarial_prompts": {
        "type": "boolean",
        "description": "Auto-generate adversarial test variants (jailbreak, injection, edge cases)"
      }
    }
  }

  arguments 62 lines copy
• mcp_server_health_check unknown 9h ago

  Check an MCP server's tool definitions without contacting it — for a server not deployed yet, a stdio server, or a CI gate. Paste the answer to tools/list (raw JSON-RPC or its result), a bare array of tools, one tool definition, or a manifest carrying name/description; input_shape says which one was read. Validates every tool against the MCP spec (name 1-128 chars of A-Z a-z 0-9 _ - .; camelCase and a tool with no parameters are valid; inputSchema of type object; required fields listed in properties) and, with strict, documentation (description ≥ 20 chars, every parameter with a description and a type). verdict: FAIL when a tool breaks a rule or no tool is found, WARN when only a manifest's server name/description is missing, PASS otherwise. A tools/list carries no server name, so those two checks are not scored on it (the name comes from initialize). Gate on verdict or on checks[tool_quality].pass.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "manifest": "{\"tools\":[{\"name\":\"get_weather\",\"description\":\"Return the current weather for a city. Use it when the user asks about the weather now.\",\"inputSchema\":{\"type\":\"object\",\"required\":[\"city\"],\"properties\":{\"city\":{\"type\":\"string\",\"description\":\"City name, e.g. Paris\"}}}}]}"
      }
    ],
    "required": [
      "manifest"
    ],
    "properties": {
      "strict": {
        "type": "boolean",
        "description": "Also require documentation: every description ≥ 20 characters, every parameter with a description and a type."
      },
      "manifest": {
        "type": "string",
        "description": "JSON text: the answer to tools/list (raw JSON-RPC or its result), an array of tools, one tool definition, or a manifest with name/description/tools."
      }
    }
  }

  arguments 21 lines copy
• consistency_check unknown 3d ago

  Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, deterministic, no API key needed. Limitations: relies on surface-level word matching — "Paris is the capital of France" vs "Paris is the French capital" may score low despite semantic equivalence. True semantic consistency: run_semantic_tests in embedding mode. Essential for determinism testing.

  mcp-tool

  123456789101112131415161718192021222324252627

  {
    "type": "object",
    "examples": [
      {
        "responses": [
          "The capital of France is Paris.",
          "Paris is the capital of France."
        ]
      }
    ],
    "required": [
      "responses"
    ],
    "properties": {
      "responses": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Array of 2+ LLM responses to compare (same prompt, different runs)"
      },
      "check_facts": {
        "type": "boolean",
        "description": "Check for contradictory numbers/facts across responses (default: true)"
      }
    }
  }

  arguments 27 lines copy
• response_quality_score unknown 6d ago

  Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actually measured — or total_score null with the reason when none is given, because relevance and correctness cannot be read off word overlap (a correct one-word answer shares no words with its question). Meaning against a reference answer: run_semantic_tests. Also returns unscored signals: question-term overlap, average sentence length, markdown.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334

  {
    "type": "object",
    "examples": [
      {
        "question": "q",
        "response": "anything"
      }
    ],
    "required": [
      "question",
      "response"
    ],
    "properties": {
      "question": {
        "type": "string",
        "description": "The original question/prompt"
      },
      "response": {
        "type": "string",
        "description": "The LLM response to score"
      },
      "max_length": {
        "type": "number",
        "description": "Maximum character length. Scored as a proportional penalty beyond it."
      },
      "expected_keywords": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Keywords a correct answer must contain, as whole words (case-insensitive; a plural in -s/-es counts). Scored as coverage."
      }
    }
  }

  arguments 34 lines copy
• toxicity_scan reads unknown 4d ago

  Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term + predicate), not a semantic classifier — returns per-category risk plus the named rules that fired, so every finding can be checked. Languages: English for every category; French for insults, coarse language, direct threats and self-harm intent; anything else reads clean, so clean is not proof of safety. Text is also matched after undoing leetspeak and lookalike letters. Self-harm is critical only on a first-person intent — a mention ("call 988 if you have suicidal thoughts") is medium. Useful for LLM safety guardrail testing and triage; signal-only, not a calibrated CI gate.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132

  {
    "type": "object",
    "examples": [
      {
        "text": "Ce contrat est conclu avec un partenaire."
      }
    ],
    "required": [
      "text"
    ],
    "properties": {
      "text": {
        "type": "string",
        "description": "Text to scan"
      },
      "categories": {
        "type": "array",
        "items": {
          "enum": [
            "profanity",
            "hate_speech",
            "violence",
            "sexual",
            "self_harm",
            "bias"
          ],
          "type": "string"
        },
        "description": "Categories to check (default: all)"
      }
    }
  }

  arguments 32 lines copy
• conversation_analyze unknown 3d ago

  Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential for chatbot QA.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142

  {
    "type": "object",
    "examples": [
      {
        "messages": [
          {
            "role": "user",
            "content": "What is your refund policy?"
          },
          {
            "role": "assistant",
            "content": "Refunds are available within 30 days."
          }
        ]
      }
    ],
    "required": [
      "messages"
    ],
    "properties": {
      "messages": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "role": {
              "enum": [
                "system",
                "user",
                "assistant"
              ],
              "type": "string"
            },
            "content": {
              "type": "string"
            }
          }
        },
        "description": "Conversation messages in order"
      }
    }
  }

  arguments 42 lines copy
• cot_analyzer unknown 3d ago

  Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning depth. Useful for o1/o3/DeepSeek-R1 evaluation.

  mcp-tool

  12345678910111213141516171819202122

  {
    "type": "object",
    "examples": [
      {
        "reasoning": "Step 1: 12 apples minus 5 eaten.\nStep 2: 12 - 5 = 7.\nTherefore, the answer is 7.",
        "expected_conclusion": "7"
      }
    ],
    "required": [
      "reasoning"
    ],
    "properties": {
      "reasoning": {
        "type": "string",
        "description": "The CoT / reasoning trace text (e.g. from <think> tags or step-by-step output)"
      },
      "expected_conclusion": {
        "type": "string",
        "description": "Expected final answer to check against (optional)"
      }
    }
  }

  arguments 22 lines copy
• bias_detect unknown 3d ago

  Analyse a set of LLM responses generated from the same prompt template but with different demographic variants (gender, origin, age, tone). Returns a bias score (0-100), sentiment analysis per variant, pairwise Jaccard similarity, and a human-readable verdict. No API key needed — runs entirely locally.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738

  {
    "type": "object",
    "examples": [
      {
        "responses": [
          "He is great.",
          "She is great."
        ]
      }
    ],
    "required": [
      "responses"
    ],
    "properties": {
      "responses": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "variantId",
            "answer"
          ],
          "properties": {
            "answer": {
              "type": "string",
              "description": "The LLM response text for this variant"
            },
            "variantId": {
              "type": "string",
              "description": "Identifier for the demographic variant (e.g. \"male\", \"female\", \"western\", \"young\")"
            }
          }
        },
        "minItems": 2,
        "description": "Array of variant responses to compare for bias"
      }
    }
  }

  arguments 38 lines copy
• ssl_certificate_check unknown 5d ago

  Analyse the SSL/TLS certificate of any HTTPS host. Returns certificate subject, issuer, validity dates, days until expiry, protocol version, cipher suite, key exchange info, and an overall grade (A+, A, B, C, F). Detects expired, self-signed, and weak certificates. Use this to audit TLS posture before production deployment or during security reviews.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "host": "www.ia-qa.com"
      }
    ],
    "required": [
      "host"
    ],
    "properties": {
      "host": {
        "type": "string",
        "description": "Hostname to check (e.g. example.com). Do not include https:// prefix."
      },
      "port": {
        "type": "number",
        "description": "Port number (default: 443)"
      }
    }
  }

  arguments 21 lines copy
• cors_test unknown 3d ago

  Audit a URL for CORS misconfigurations across several origins: wildcard with credentials, reflected origin, null origin, subdomain bypass, missing Vary. Returns risk level (safe/low/medium/high/critical) plus per-origin results. "unknown" means nothing was tested: every origin failed to connect, answered 5xx, or was refused with no CORS header (a 404, a firewall); never read it as "safe".

  mcp-tool

  12345678910111213141516171819202122

  {
    "type": "object",
    "examples": [
      {
        "url": "https://www.ia-qa.com/mcp",
        "origin": "https://other.example"
      }
    ],
    "required": [
      "url"
    ],
    "properties": {
      "url": {
        "type": "string",
        "description": "Full URL to test (e.g. https://api.example.com/endpoint)"
      },
      "origin": {
        "type": "string",
        "description": "Custom Origin header to test (default: tests multiple origins automatically)"
      }
    }
  }

  arguments 22 lines copy
• cors_checker unknown 3d ago

  Would a browser on this origin be allowed to read this URL? Verdict allowed | blocked | unknown, the reason, and the header to add. Only GET and HEAD are really sent: other methods measure the preflight alone. Many origins at once: cors_test.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536

  {
    "type": "object",
    "examples": [
      {
        "url": "https://www.ia-qa.com/mcp"
      }
    ],
    "required": [
      "url"
    ],
    "properties": {
      "url": {
        "type": "string",
        "description": "Full URL to test, e.g. https://api.example.com/resource"
      },
      "method": {
        "type": "string",
        "description": "Method the page uses (default: GET)"
      },
      "origin": {
        "type": "string",
        "description": "Origin of the calling page (default: https://yourdomain.com)"
      },
      "credentials": {
        "type": "boolean",
        "description": "True for fetch with credentials: \"include\""
      },
      "request_headers": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Headers the page sets, e.g. [\"Authorization\", \"Content-Type: application/json\"]. Names only are sent."
      }
    }
  }

  arguments 36 lines copy
• web_security_audit unknown 3d ago

  Run a comprehensive web security audit combining headers, SSL, CORS, and cookies checks — then use an LLM to produce a prioritised remediation plan. Orchestrates security_headers_check + ssl_certificate_check + cors_test + cookie_security_audit in parallel, merges all findings, then asks an AI model to: (1) rank vulnerabilities by real-world exploitability, (2) generate a remediation roadmap, (3) produce fix code snippets for the detected stack. Returns both raw audit data and the AI analysis. Use this as a one-click security posture assessment.

  mcp-tool

  1234567891011121314151617181920212223242526

  {
    "type": "object",
    "examples": [
      {
        "url": "https://www.ia-qa.com",
        "model": "none"
      }
    ],
    "required": [
      "url"
    ],
    "properties": {
      "url": {
        "type": "string",
        "description": "Full URL to audit (e.g. https://example.com)"
      },
      "model": {
        "type": "string",
        "description": "LLM model for AI analysis (default: \"openai/gpt-oss-20b\"). Set to \"none\" to skip AI analysis."
      },
      "api_key": {
        "type": "string",
        "description": "Your Groq or HuggingFace API key. Required to enable AI analysis."
      }
    }
  }

  arguments 26 lines copy
• rag_relevance_rank reads unknown 6d ago

  Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233

  {
    "type": "object",
    "examples": [
      {
        "query": "quantum",
        "chunks": [
          "a b c",
          "d e f"
        ]
      }
    ],
    "required": [
      "query",
      "chunks"
    ],
    "properties": {
      "query": {
        "type": "string",
        "description": "The user query"
      },
      "top_k": {
        "type": "number",
        "description": "Return top K results (default: all)"
      },
      "chunks": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Array of text chunks to rank"
      }
    }
  }

  arguments 33 lines copy
• vector_quantize unknown 3d ago

  Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.

  mcp-tool

  1234567891011121314151617181920212223242526272829

  {
    "type": "object",
    "examples": [
      {
        "vector": [
          0.1,
          -0.5,
          0.9,
          0
        ]
      }
    ],
    "required": [
      "vector"
    ],
    "properties": {
      "bits": {
        "type": "number",
        "description": "Quantization bits: 8 (int8, default) or 4 (int4)"
      },
      "vector": {
        "type": "array",
        "items": {
          "type": "number"
        },
        "description": "Float32 vector to quantize"
      }
    }
  }

  arguments 29 lines copy
• vector_stats unknown 6d ago

  Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and analyzing vector distributions in a vector DB.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748

  {
    "type": "object",
    "anyOf": [
      {
        "required": [
          "vector"
        ]
      },
      {
        "required": [
          "matrix"
        ]
      }
    ],
    "examples": [
      {
        "vector": [
          1,
          2,
          3,
          4
        ]
      }
    ],
    "properties": {
      "top_k": {
        "type": "number",
        "description": "Return indices of top K absolute values (default: 5)"
      },
      "matrix": {
        "type": "array",
        "items": {
          "type": "array",
          "items": {
            "type": "number"
          }
        },
        "description": "Matrix of vectors (overrides vector). Returns per-vector + matrix-level stats. Required unless `vector` is given."
      },
      "vector": {
        "type": "array",
        "items": {
          "type": "number"
        },
        "description": "Single vector to analyze. Required unless `matrix` is given."
      }
    }
  }

  arguments 48 lines copy
• bm25_score unknown 3d ago

  Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, OpenSearch, and Weaviate hybrid search. Returns ranked results with normalized scores.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142

  {
    "type": "object",
    "examples": [
      {
        "query": "refund damaged",
        "documents": [
          "shipping times",
          "refund for damaged items",
          "office hours"
        ]
      }
    ],
    "required": [
      "query",
      "documents"
    ],
    "properties": {
      "b": {
        "type": "number",
        "description": "Length normalization factor (default: 0.75)"
      },
      "k1": {
        "type": "number",
        "description": "Term frequency saturation (default: 1.5)"
      },
      "query": {
        "type": "string",
        "description": "The search query"
      },
      "top_k": {
        "type": "number",
        "description": "Return top K results (default: all)"
      },
      "documents": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Array of documents to rank"
      }
    }
  }

  arguments 42 lines copy
• build_rag_prompt unknown 3d ago

  Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, system instruction injection, and source attribution.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758

  {
    "type": "object",
    "examples": [
      {
        "query": "q?",
        "chunks": [
          {
            "text": "c1 text",
            "source": "a.md"
          }
        ]
      }
    ],
    "required": [
      "query",
      "chunks"
    ],
    "properties": {
      "query": {
        "type": "string",
        "description": "The user question to answer"
      },
      "chunks": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "text": {
              "type": "string"
            },
            "score": {
              "type": "number"
            },
            "source": {
              "type": "string"
            }
          }
        },
        "description": "Retrieved context chunks with .text (required), .source (optional), .score (optional)"
      },
      "language": {
        "type": "string",
        "description": "Response language instruction (e.g. \"French\", \"Spanish\")"
      },
      "cite_sources": {
        "type": "boolean",
        "description": "Add [1], [2] citation numbers (default: true)"
      },
      "max_context_tokens": {
        "type": "number",
        "description": "Max tokens for context section (default: 2000)"
      },
      "system_instruction": {
        "type": "string",
        "description": "Custom system instruction (default: standard RAG grounding instruction)"
      }
    }
  }

  arguments 58 lines copy
• prompt_template_fill unknown 6d ago

  Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled variables.

  mcp-tool

  1234567891011121314151617181920212223242526272829

  {
    "type": "object",
    "examples": [
      {
        "template": "Hi {{name}}",
        "variables": {
          "name": "A"
        }
      }
    ],
    "required": [
      "template"
    ],
    "properties": {
      "strict": {
        "type": "boolean",
        "description": "Throw error if any variable is not provided (default: false)"
      },
      "template": {
        "type": "string",
        "description": "Prompt template with {{variable}} placeholders"
      },
      "variables": {
        "type": "object",
        "description": "Key-value pairs to fill (e.g. {\"name\":\"Alice\",\"role\":\"engineer\"})",
        "additionalProperties": true
      }
    }
  }

  arguments 29 lines copy
• system_prompt_builder unknown 5d ago

  Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prompt with token estimate.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455

  {
    "type": "object",
    "examples": [
      {
        "role": "support agent",
        "constraints": [
          "no PII"
        ],
        "output_format": "json"
      }
    ],
    "required": [
      "role"
    ],
    "properties": {
      "role": {
        "type": "string",
        "description": "Role/persona (e.g. \"Senior QA Engineer\", \"JSON extraction assistant\")"
      },
      "task": {
        "type": "string",
        "description": "Main task or objective"
      },
      "tone": {
        "enum": [
          "professional",
          "friendly",
          "concise",
          "technical",
          "educational"
        ],
        "type": "string",
        "description": "Communication tone"
      },
      "examples": {
        "type": "string",
        "description": "Brief examples to include"
      },
      "language": {
        "type": "string",
        "description": "Response language (e.g. \"French\")"
      },
      "constraints": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Rules and constraints to follow"
      },
      "output_format": {
        "type": "string",
        "description": "Expected output format description"
      }
    }
  }

  arguments 55 lines copy
• count_tokens reads unknown 3d ago

  Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Shows whether a text fits the context window of a model and what it costs. Returns token estimate, character count, and word count.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "Hello world, this is a test."
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to count tokens for"
      }
    }
  }

  arguments 17 lines copy
• truncate_to_tokens unknown never probed

  Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the start (useful for keeping recent conversation history). Reports whether truncation occurred and the estimated token count.

  mcp-tool

  123456789101112131415161718192021222324252627

  {
    "type": "object",
    "examples": [
      {
        "input": "short",
        "max_tokens": 50
      }
    ],
    "required": [
      "input",
      "max_tokens"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to truncate"
      },
      "from_end": {
        "type": "boolean",
        "description": "Keep the end of the text instead of the start (default: false)"
      },
      "max_tokens": {
        "type": "number",
        "description": "Maximum number of tokens to keep"
      }
    }
  }

  arguments 27 lines copy
• split_chunks reads unknown 5d ago

  Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines.

  mcp-tool

  12345678910111213141516171819202122232425262728

  {
    "type": "object",
    "examples": [
      {
        "input": "Refunds are accepted within 30 days. Damaged items are replaced for free. Contact support to start a return.",
        "overlap": 2,
        "chunk_tokens": 12
      }
    ],
    "required": [
      "input",
      "chunk_tokens"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to split into chunks"
      },
      "overlap": {
        "type": "number",
        "description": "Token overlap between consecutive chunks (default: 0)"
      },
      "chunk_tokens": {
        "type": "number",
        "description": "Maximum tokens per chunk (10–8000)"
      }
    }
  }

  arguments 28 lines copy
• estimate_llm_cost reads unknown 2d ago

  Estimate the API cost in USD for a given model and token counts, at the price read daily from the provider's own pricing page (prices_as_of, price_status). Any model from list_llm_models.

  mcp-tool

  12345678910111213141516171819202122232425262728

  {
    "type": "object",
    "examples": [
      {
        "model": "gpt-4o",
        "input_tokens": 1000000,
        "output_tokens": 1000000
      }
    ],
    "required": [
      "model",
      "input_tokens"
    ],
    "properties": {
      "model": {
        "type": "string",
        "description": "Model name, e.g. \"gpt-4o\", \"claude-3.5-sonnet\", \"deepseek-v3\""
      },
      "input_tokens": {
        "type": "number",
        "description": "Number of input/prompt tokens"
      },
      "output_tokens": {
        "type": "number",
        "description": "Number of output/completion tokens (default: 0)"
      }
    }
  }

  arguments 28 lines copy
• token_budget_calculator unknown 4d ago

  Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds the model's context window (any model from list_llm_models).

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647

  {
    "type": "object",
    "examples": [
      {
        "model": "gpt-4o",
        "context_tokens": 1000,
        "expected_output_tokens": 500
      }
    ],
    "required": [
      "model"
    ],
    "properties": {
      "model": {
        "type": "string",
        "description": "Model id from list_llm_models (e.g. gpt-6.1-sol, claude-sonnet-5-5, gemini-3.8-flash)"
      },
      "context": {
        "type": "string",
        "description": "Actual context text (will estimate tokens)"
      },
      "user_input": {
        "type": "string",
        "description": "Actual user input text (will estimate tokens)"
      },
      "system_prompt": {
        "type": "string",
        "description": "Actual system prompt text (will estimate tokens)"
      },
      "context_tokens": {
        "type": "number",
        "description": "Token count for RAG context / documents"
      },
      "user_input_tokens": {
        "type": "number",
        "description": "Token count for user message"
      },
      "system_prompt_tokens": {
        "type": "number",
        "description": "Token count for system prompt"
      },
      "expected_output_tokens": {
        "type": "number",
        "description": "Expected max output tokens"
      }
    }
  }

  arguments 47 lines copy
• context_window_check reads unknown 3d ago

  Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncation risk.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243

  {
    "type": "object",
    "examples": [
      {
        "model": "gpt-4o",
        "messages": [
          {
            "role": "user",
            "content": "hi"
          }
        ]
      }
    ],
    "required": [
      "messages",
      "model"
    ],
    "properties": {
      "model": {
        "type": "string",
        "description": "Model id from list_llm_models (e.g. gpt-6.1-sol, claude-sonnet-5-5)"
      },
      "messages": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "role": {
              "type": "string"
            },
            "content": {
              "type": "string"
            }
          }
        },
        "description": "Array of messages (system/user/assistant)"
      },
      "max_output_tokens": {
        "type": "number",
        "description": "Reserved tokens for output (default: 4096)"
      }
    }
  }

  arguments 43 lines copy
• optimize_prompt_tokens unknown 6d ago

  Compress an LLM prompt by removing filler words, verbose phrases, duplicate sentences, and unnecessary whitespace. Returns optimized text with token savings breakdown. 100% deterministic, no API key needed.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839

  {
    "type": "object",
    "examples": [
      {
        "text": "Please   kindly    make sure that you   basically   summarize   the following text."
      }
    ],
    "required": [
      "text"
    ],
    "properties": {
      "text": {
        "type": "string",
        "description": "The prompt text to optimize"
      },
      "options": {
        "type": "object",
        "properties": {
          "fillers": {
            "type": "boolean",
            "default": true
          },
          "duplicates": {
            "type": "boolean",
            "default": true
          },
          "whitespace": {
            "type": "boolean",
            "default": true
          },
          "instructions": {
            "type": "boolean",
            "default": true
          }
        },
        "description": "Toggle optimization steps (all true by default)"
      }
    }
  }

  arguments 39 lines copy
• post_jira_comment unknown never probed

  Post the output of jira_to_test_suite as a formatted comment on the source Jira ticket. Converts Gherkin, E2E steps, API tests, and ambiguities into Atlassian Document Format (ADF). STATEFUL — creates a comment on the issue.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546

  {
    "type": "object",
    "examples": [
      {
        "issue_key": "PROJ-1",
        "jira_email": "[email protected]",
        "jira_token": "<your-atlassian-token>",
        "test_suite": {
          "acceptance_criteria": [
            "A reset link expires 30 minutes after it is sent."
          ]
        },
        "jira_base_url": "https://yourcompany.atlassian.net"
      }
    ],
    "required": [
      "issue_key",
      "jira_base_url",
      "jira_email",
      "jira_token",
      "test_suite"
    ],
    "properties": {
      "issue_key": {
        "type": "string",
        "description": "Jira issue key, e.g. \"PROJ-123\""
      },
      "jira_email": {
        "type": "string",
        "description": "Atlassian account email"
      },
      "jira_token": {
        "type": "string",
        "description": "Atlassian API token"
      },
      "test_suite": {
        "type": "object",
        "description": "The test_suite object from jira_to_test_suite result",
        "additionalProperties": true
      },
      "jira_base_url": {
        "type": "string",
        "description": "Atlassian base URL"
      }
    }
  }

  arguments 46 lines copy
• fetch_jira_issue unknown 2d ago

  Fetch a complete Jira issue: summary, description converted to Markdown, status, assignee, priority, labels, custom fields, and optionally comments and attachment metadata. BYOK — credentials transit in-memory only, never stored on ia-qa.com.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950

  {
    "type": "object",
    "examples": [
      {
        "issue_key": "PROJ-1",
        "jira_email": "[email protected]",
        "jira_token": "<your-atlassian-token>",
        "jira_base_url": "https://yourcompany.atlassian.net"
      }
    ],
    "required": [
      "issue_key",
      "jira_base_url",
      "jira_email",
      "jira_token"
    ],
    "properties": {
      "fields": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Specific Jira field names to return. Omit for all standard fields."
      },
      "issue_key": {
        "type": "string",
        "description": "Jira issue key, e.g. \"PROJ-123\""
      },
      "jira_email": {
        "type": "string",
        "description": "Atlassian account email"
      },
      "jira_token": {
        "type": "string",
        "description": "Atlassian API token (from id.atlassian.com > Security > API tokens)"
      },
      "jira_base_url": {
        "type": "string",
        "description": "Atlassian base URL, e.g. \"https://mycompany.atlassian.net\""
      },
      "include_comments": {
        "type": "boolean",
        "description": "Include issue comments, up to 20 (default: true)"
      },
      "include_attachments": {
        "type": "boolean",
        "description": "Include attachment metadata list (default: false)"
      }
    }
  }

  arguments 50 lines copy
• search_jira_issues unknown 6d ago

  Search Jira using JQL (Jira Query Language). Returns matching issues with key fields. Ideal for finding open bugs, sprint tickets, or issues by label/assignee/component. BYOK — credentials transit in-memory only, never stored.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546

  {
    "type": "object",
    "examples": [
      {
        "jql": "project = PROJ AND status = \"To Do\"",
        "jira_email": "[email protected]",
        "jira_token": "<your-atlassian-token>",
        "jira_base_url": "https://yourcompany.atlassian.net"
      }
    ],
    "required": [
      "jql",
      "jira_base_url",
      "jira_email",
      "jira_token"
    ],
    "properties": {
      "jql": {
        "type": "string",
        "description": "JQL query string, e.g. \"project = PROJ AND status = Open AND assignee = currentUser() ORDER BY priority DESC\""
      },
      "fields": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Fields per issue. Default: summary, status, assignee, priority, issuetype, labels, created, updated"
      },
      "jira_email": {
        "type": "string",
        "description": "Atlassian account email"
      },
      "jira_token": {
        "type": "string",
        "description": "Atlassian API token"
      },
      "max_results": {
        "type": "number",
        "description": "Max issues to return (default: 10, max: 50)"
      },
      "jira_base_url": {
        "type": "string",
        "description": "Atlassian base URL, e.g. \"https://mycompany.atlassian.net\""
      }
    }
  }

  arguments 46 lines copy
• rate_tool unknown never probed

  Give honest usage feedback on an IA-QA MCP tool. Give a score (1-5) and a comment. Rate low (1-2) if the tool was wrong, irrelevant, or a poor fit; rate high (4-5) only if it genuinely solved your need. Maintainers read ratings, one vote per agent and tool a month: comments and per-tool scores are not published, only the overall average and vote count. Comments are screened: one carrying a credential is not stored, personal data becomes placeholders — describe what happened, not what a model should do. Skip routine successes: signal, not praise. Example: rate_tool({ tool_name: "format_json", score: 2, comment: "Tried to pretty-print a JSON5 file, it rejected trailing commas — not usable for my case." })

  mcp-tool

  123456789101112131415161718192021222324252627282930

  {
    "type": "object",
    "examples": [
      {
        "score": 4,
        "comment": "Formatted a 2 MB payload correctly.",
        "tool_name": "format_json"
      }
    ],
    "required": [
      "tool_name",
      "score"
    ],
    "properties": {
      "score": {
        "type": "number",
        "maximum": 5,
        "minimum": 1,
        "description": "Rating from 1 (poor) to 5 (excellent)"
      },
      "comment": {
        "type": "string",
        "description": "Strongly encouraged — explain what you were trying to do and whether the tool got you there. Be specific about what was missing, wrong, or a poor fit. This is the most valuable part of the rating. Up to 2000 chars are stored; go over and the response says so (truncated: true) — send the remainder as a second call rather than assuming it landed."
      },
      "tool_name": {
        "type": "string",
        "description": "Name of the MCP tool to rate (e.g. \"format_json\", \"shield_analyze\")"
      }
    }
  }

  arguments 30 lines copy
• validate_email unknown never probed

  Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — use this to avoid bounces and malformed data.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "email": "[email protected]"
      }
    ],
    "required": [
      "email"
    ],
    "properties": {
      "email": {
        "type": "string",
        "description": "Email address to validate"
      }
    }
  }

  arguments 17 lines copy
• cron_validator unknown 2d ago

  Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Returns parsed fields, a human-readable description, and upcoming ISO timestamps.

  mcp-tool

  12345678910111213141516171819202122

  {
    "type": "object",
    "examples": [
      {
        "expression": "0 0 29 2 *",
        "next_runs_count": 1
      }
    ],
    "required": [
      "expression"
    ],
    "properties": {
      "expression": {
        "type": "string",
        "description": "Cron expression with 5 fields, e.g. \"*/15 9-18 * * 1-5\""
      },
      "next_runs_count": {
        "type": "number",
        "description": "How many upcoming runs to return (1-50, default: 10)"
      }
    }
  }

  arguments 22 lines copy
• http_status_lookup unknown 31h ago

  Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code in the IANA HTTP Status Code Registry (1xx-5xx, including 226, 425, 451, 508, 511 and the WebDAV codes) with its defining RFC; anything outside the registry is reported as `registered: false` rather than described. `cacheable` means heuristically cacheable by default per RFC 9110 §15.1.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "code": 418
      }
    ],
    "required": [
      "code"
    ],
    "properties": {
      "code": {
        "type": "number",
        "description": "HTTP status code (e.g. 200, 404, 429, 503)"
      }
    }
  }

  arguments 17 lines copy
• generate_curl unknown 41h ago

  Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and debugging API calls.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344

  {
    "type": "object",
    "examples": [
      {
        "url": "https://x.com",
        "method": "DELETE"
      }
    ],
    "required": [
      "url"
    ],
    "properties": {
      "url": {
        "type": "string",
        "description": "Request URL (must be http/https)"
      },
      "body": {
        "type": "string",
        "description": "Raw request body string"
      },
      "method": {
        "type": "string",
        "description": "HTTP method (default: GET)"
      },
      "headers": {
        "type": "object",
        "description": "Request headers as key-value object",
        "additionalProperties": true
      },
      "verbose": {
        "type": "boolean",
        "description": "Add -v for verbose output (default: false)"
      },
      "body_json": {
        "type": "object",
        "description": "JSON body (auto-adds Content-Type: application/json)",
        "additionalProperties": true
      },
      "follow_redirects": {
        "type": "boolean",
        "description": "Follow redirects with -L flag (default: true)"
      }
    }
  }

  arguments 44 lines copy
• extract_todos unknown 2d ago

  Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for technical debt auditing.

  mcp-tool

  12345678910111213141516171819202122232425262728

  {
    "type": "object",
    "examples": [
      {
        "input": "// todo: refactor\n// todo(jc): rename"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "tags": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Custom tags to add (default set: TODO, FIXME, HACK, NOTE, BUG, OPTIMIZE, XXX)"
      },
      "input": {
        "type": "string",
        "description": "Code or text to scan"
      },
      "include_context": {
        "type": "boolean",
        "description": "Include full line text (default: true)"
      }
    }
  }

  arguments 28 lines copy
• lint_commit_message unknown 16h ago

  Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, breaking change detection, and actionable suggestions.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "message": "feat!: drop node 18"
      }
    ],
    "required": [
      "message"
    ],
    "properties": {
      "strict": {
        "type": "boolean",
        "description": "Enforce strict rules: max 72-char subject, imperative mood check (default: false)"
      },
      "message": {
        "type": "string",
        "description": "Git commit message to validate"
      }
    }
  }

  arguments 21 lines copy
• pr_gatekeeper reads unknown 6d ago

  Compound quality gate for pull requests. Runs three sequential checks: (1) secret detection — scans diff for API keys, tokens, passwords matching 16 regex patterns; (2) bug analysis — heuristic scan for eval(), innerHTML, empty catch, console.log, TODO/FIXME; (3) commit message linting against Conventional Commits spec. Returns gate verdict (PASS/WARN/BLOCK), blockers, and actionable warnings.

  mcp-tool

  123456789101112131415161718192021222324252627

  {
    "type": "object",
    "examples": [
      {
        "diff": "diff --git a/pay.js b/pay.js\n--- a/pay.js\n+++ b/pay.js\n@@ -1 +1 @@\n-if (amount > 0) charge(amount);\n+if (amount >= 0) charge(amount);",
        "commit_message": "fix(pay): accept a zero amount"
      }
    ],
    "required": [
      "diff",
      "commit_message"
    ],
    "properties": {
      "diff": {
        "type": "string",
        "description": "Unified git diff (output of `git diff HEAD`)"
      },
      "context": {
        "type": "string",
        "description": "Optional: PR title or description for richer bug analysis"
      },
      "commit_message": {
        "type": "string",
        "description": "The commit message to lint (e.g. \"feat(auth): add OAuth2 login\")"
      }
    }
  }

  arguments 27 lines copy
• webhook_endpoint_create unknown never probed

  Create a temporary webhook endpoint that captures incoming HTTP requests for one hour. Returns the webhook id, public URL, expiration timestamp, and current request count. Use together with webhook_endpoint_requests to inspect captured payloads.

  mcp-tool

  123456789101112

  {
    "type": "object",
    "examples": [
      {}
    ],
    "properties": {
      "base_url": {
        "type": "string",
        "description": "Optional public base URL. Default: https://www.ia-qa.com/mcp/webhook (the apex ia-qa.com answers 301 and a redirected POST loses its body, so an apex base_url is normalized to www)"
      }
    }
  }

  arguments 12 lines copy
• webhook_endpoint_requests unknown 3d ago

  Fetch the requests captured by a webhook created with webhook_endpoint_create. Returns the newest requests first with method, headers, query params, body payload, and timestamps.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "id": "<id returned by webhook_endpoint_create>"
      }
    ],
    "required": [
      "id"
    ],
    "properties": {
      "id": {
        "type": "string",
        "description": "Webhook id returned by webhook_endpoint_create"
      },
      "limit": {
        "type": "number",
        "description": "Maximum number of requests to return (1-100, default: 20)"
      }
    }
  }

  arguments 21 lines copy
• env_parse unknown 2d ago

  Parse a .env file content into a JSON object. Handles quoted values (single and double), inline comments, export prefix, and escaped sequences (\n, \t inside double quotes). Returns all key-value pairs. Use in CI/CD pipelines, agent config loaders, or when processing dotenv files programmatically.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "PORT=3000"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": ".env file content to parse (e.g. the output of `cat .env`)"
      }
    }
  }

  arguments 17 lines copy
• generate_slug unknown 37h ago

  Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO-friendly URL paths, file names, or identifier keys from user-provided titles or labels.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "input": "Héllo Wörld 2026"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "String to slugify"
      },
      "separator": {
        "type": "string",
        "description": "Separator character (default: \"-\")"
      }
    }
  }

  arguments 21 lines copy
• score_geo_signals unknown never probed

  Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. GEO = optimizing pages for AI-powered search engines (ChatGPT Search, Perplexity, etc.).

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "head_html": "<head><title>Refund policy | Shop</title><meta name=\"description\" content=\"How refunds work at Shop.\"></head>"
      }
    ],
    "required": [
      "head_html"
    ],
    "properties": {
      "head_html": {
        "type": "string",
        "description": "Raw HTML of the <head> section (or full page HTML) to analyze"
      }
    }
  }

  arguments 17 lines copy
• latency_benchmark unknown 20h ago

  Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarking.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556

  {
    "type": "object",
    "examples": [
      {
        "endpoints": "https://www.ia-qa.com/mcp/ping",
        "iterations": 3
      }
    ],
    "required": [
      "endpoints"
    ],
    "properties": {
      "endpoints": {
        "type": [
          "string",
          "array"
        ],
        "items": {
          "type": "object",
          "properties": {
            "url": {
              "type": "string",
              "description": "Full URL to test"
            },
            "body": {
              "type": "object",
              "description": "Request body for POST",
              "additionalProperties": true
            },
            "label": {
              "type": "string",
              "description": "Optional label for this endpoint"
            },
            "method": {
              "enum": [
                "GET",
                "POST"
              ],
              "type": "string",
              "description": "HTTP method (default: GET)"
            },
            "headers": {
              "type": "object",
              "description": "Custom headers",
              "additionalProperties": true
            }
          }
        },
        "description": "Endpoints to benchmark. Accepts a single URL string, an array of URL strings, or an array of {url, method?, body?, headers?, label?} objects."
      },
      "iterations": {
        "type": "number",
        "description": "Number of iterations per endpoint (default: 3, max: 10)"
      }
    }
  }

  arguments 56 lines copy
• format_json unknown 47h ago

  Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejected with the exact parse error. It never repairs, completes, or guesses. NOT for: plain text or prose (will fail), JSON embedded in markdown/prose (extract_json_from_text pulls it out), JS objects (JSON.stringify them first), YAML (yaml_to_json reads it).

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "input": "{\"a\":1,\"b\":{\"a\":2}}"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "A raw JSON string, e.g. '{\"key\":\"value\"}'. Must already parse as JSON — plain text or truncated JSON is rejected, not repaired."
      },
      "indent": {
        "type": "number",
        "description": "Indent size (default: 2)"
      }
    }
  }

  arguments 21 lines copy
• extract_json_path unknown 2d ago

  Extract a value from a JSON string using dot-notation path (e.g., "user.address.city", "items.0.name", "meta.tags"). Supports array index access via numeric path segments.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738

  {
    "type": "object",
    "examples": [
      {
        "path": "a.b.0.c",
        "input": {
          "a": {
            "b": [
              {
                "c": 1
              },
              {
                "c": 2
              }
            ]
          }
        }
      }
    ],
    "required": [
      "input",
      "path"
    ],
    "properties": {
      "path": {
        "type": "string",
        "description": "Dot-notation path, e.g. \"user.address.city\" or \"items.0.name\""
      },
      "input": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The JSON to traverse — a JSON string, or the object/array itself."
      }
    }
  }

  arguments 38 lines copy
• generate_json_ld unknown 37h ago

  Generate a ready-to-paste <script type="application/ld+json"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person, Organization, SoftwareApplication, HowTo.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243

  {
    "type": "object",
    "examples": [
      {
        "type": "FAQPage",
        "faq_items": [
          {
            "answer": "A.",
            "question": "Q?"
          }
        ]
      }
    ],
    "required": [
      "type"
    ],
    "properties": {
      "type": {
        "type": "string",
        "description": "Schema @type: \"WebSite\", \"FAQPage\", \"Article\", \"Person\", \"Organization\", \"SoftwareApplication\", \"HowTo\""
      },
      "fields": {
        "type": "object",
        "description": "Schema fields as key-value pairs (name, url, description, author, datePublished, etc.)",
        "additionalProperties": true
      },
      "faq_items": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "answer": {
              "type": "string"
            },
            "question": {
              "type": "string"
            }
          }
        },
        "description": "For FAQPage/HowTo: array of { question, answer } objects"
      }
    }
  }

  arguments 43 lines copy
• json_schema_validate unknown 23h ago

  Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date/date-time/uuid), minimum/maximum, minLength/maxLength, minItems/maxItems, uniqueItems, additionalProperties, anyOf, allOf, oneOf, not, and local $ref (#/definitions/…, #/$defs/…). A schema using a keyword outside that list (patternProperties, if/then, dependencies, a remote $ref…) is never reported valid: the keyword is named in `unevaluated_keywords`. Returns all validation errors with dot-notation paths.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152

  {
    "type": "object",
    "examples": [
      {
        "value": {
          "p": {
            "n": "five"
          }
        },
        "schema": {
          "type": "object",
          "$defs": {
            "P": {
              "type": "object",
              "properties": {
                "n": {
                  "type": "string"
                }
              }
            }
          },
          "properties": {
            "p": {
              "$ref": "#/$defs/P"
            }
          }
        }
      }
    ],
    "required": [
      "value",
      "schema"
    ],
    "properties": {
      "value": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The JSON value to validate — a JSON string, or the value itself."
      },
      "schema": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The JSON Schema — a JSON string, or the schema object itself."
      }
    }
  }

  arguments 52 lines copy
• flatten_json reads unknown 47h ago

  Flatten a nested JSON object to single-level dot-notation keys (e.g. {"a":{"b":1}} → {"a.b":1}), or unflatten dot-notation keys back to a nested object. Supports custom separators.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637

  {
    "type": "object",
    "examples": [
      {
        "input": {
          "a": {
            "b": 1,
            "c": [
              1,
              2
            ]
          }
        }
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "mode": {
        "type": "string",
        "description": "\"flatten\" (default) or \"unflatten\""
      },
      "input": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The JSON to flatten or unflatten — a JSON string, or the object itself."
      },
      "separator": {
        "type": "string",
        "description": "Key separator (default: \".\")"
      }
    }
  }

  arguments 37 lines copy
• xml_to_json unknown 3d ago

  Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attributes.

  mcp-tool

  1234567891011121314151617181920212223242526272829

  {
    "type": "object",
    "examples": [
      {
        "input": "<r a=\"1\"><i>x</i><i>y</i></r>"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "XML string to convert"
      },
      "attr_prefix": {
        "type": "string",
        "description": "Prefix for attribute keys (default: \"@_\")"
      },
      "ignore_attrs": {
        "type": "boolean",
        "description": "Ignore XML attributes (default: false)"
      },
      "parse_values": {
        "type": "boolean",
        "description": "Auto-parse numbers and booleans (default: true)"
      }
    }
  }

  arguments 29 lines copy
• transform_json_array unknown 4d ago

  Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), uniq_by (field). Useful for processing MCP tool results and LLM structured outputs.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667

  {
    "type": "object",
    "examples": [
      {
        "field": "v",
        "input": [
          {
            "v": 2
          },
          {
            "v": 10
          },
          {
            "v": 1
          }
        ],
        "operation": "sort_by",
        "sort_order": "desc"
      }
    ],
    "required": [
      "input",
      "operation"
    ],
    "properties": {
      "n": {
        "type": "number",
        "description": "For first_n / last_n: number of items"
      },
      "path": {
        "type": "string",
        "description": "Optional dot-notation path to the array within the JSON object (e.g. \"data.items\")"
      },
      "field": {
        "type": "string",
        "description": "Field to operate on (for sort_by, group_by, count_by, uniq_by, filter)"
      },
      "input": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The JSON containing an array (or an object with an array at `path`) — a JSON string, or the value itself."
      },
      "fields": {
        "type": "string",
        "description": "Comma-separated field list for \"pluck\" (e.g. \"id,name,email\")"
      },
      "filter_op": {
        "type": "string",
        "description": "For \"filter\": \"==\" | \"!=\" | \">\" | \">=\" | \"<\" | \"<=\" | \"contains\" | \"exists\" | \"!exists\""
      },
      "operation": {
        "type": "string",
        "description": "Operation: \"pluck\", \"filter\", \"sort_by\", \"group_by\", \"count_by\", \"uniq_by\", \"reverse\", \"first_n\", \"last_n\", \"flatten\""
      },
      "sort_order": {
        "type": "string",
        "description": "For sort_by: \"asc\" (default) or \"desc\""
      },
      "filter_value": {
        "type": "string",
        "description": "For \"filter\": value to compare against"
      }
    }
  }

  arguments 67 lines copy
• json_to_csv unknown 23h ago

  Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536

  {
    "type": "object",
    "examples": [
      {
        "input": [
          {
            "a": 1
          },
          {
            "b": 2
          }
        ]
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The array of objects to convert — a JSON string, or the array itself."
      },
      "headers": {
        "type": "boolean",
        "description": "Include header row (default: true)"
      },
      "delimiter": {
        "type": "string",
        "description": "Column delimiter (default: \",\")"
      }
    }
  }

  arguments 36 lines copy
• json_to_yaml reads unknown 20h ago

  Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies.

  mcp-tool

  123456789101112131415161718192021222324252627282930

  {
    "type": "object",
    "examples": [
      {
        "input": {
          "a": "x: y",
          "c": null,
          "d": "yes",
          "e": "0123"
        }
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The JSON to convert to YAML — a JSON string, or the value itself."
      },
      "indent": {
        "type": "number",
        "description": "Indentation size in spaces (default: 2)"
      }
    }
  }

  arguments 30 lines copy
• format_table unknown 44h ago

  Convert a JSON array of objects into a Markdown table. Automatically detects columns, aligns headers, and fills missing keys with empty cells. Use when an agent needs to present structured data — tool results, model comparisons, test reports — as a readable table in a response or document.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132

  {
    "type": "object",
    "examples": [
      {
        "input": [
          {
            "a": 1
          }
        ]
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The array of objects to render — a JSON string, or the array itself."
      },
      "columns": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Column names and order (default: all keys from first row)"
      }
    }
  }

  arguments 32 lines copy
• text_stats unknown 5d ago

  Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated reading time in minutes. Sentence counting is abbreviation-aware — titles (Mr., Dr.), acronyms (U.S., i.e., p.m.), initials, decimals, URLs and emails do not end a sentence, and a text with no terminal punctuation still counts as one. Use for validating form field lengths, evaluating LLM output verbosity, or content auditing.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "One two three.\nFour five!\n\nSix?"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "The text to analyse"
      }
    }
  }

  arguments 17 lines copy
• case_convert unknown 3d ago

  Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generation and refactoring.

  mcp-tool

  1234567891011121314151617181920212223

  {
    "type": "object",
    "examples": [
      {
        "to": "snake",
        "input": "helloWorld fooBar"
      }
    ],
    "required": [
      "input",
      "to"
    ],
    "properties": {
      "to": {
        "type": "string",
        "description": "Target case: \"camel\", \"pascal\", \"snake\", \"kebab\", \"upper_snake\", \"dot\", \"title\""
      },
      "input": {
        "type": "string",
        "description": "String to convert (e.g., \"myVariableName\", \"my-css-class\")"
      }
    }
  }

  arguments 23 lines copy
• sort_lines unknown 5d ago

  Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334

  {
    "type": "object",
    "examples": [
      {
        "input": "b\na\nb\nc",
        "operation": "deduplicate"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "trim": {
        "type": "boolean",
        "description": "Trim whitespace from each line (default: true)"
      },
      "input": {
        "type": "string",
        "description": "Multi-line text to process"
      },
      "filter": {
        "type": "string",
        "description": "For \"filter\": keep lines containing this substring (case-insensitive)"
      },
      "operation": {
        "type": "string",
        "description": "\"sort\" (default), \"sort_desc\", \"reverse\", \"deduplicate\", \"unique_sort\", \"filter\""
      },
      "remove_empty": {
        "type": "boolean",
        "description": "Remove empty lines (default: true)"
      }
    }
  }

  arguments 34 lines copy
• html_to_markdown reads unknown 31h ago

  Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web content as LLM context.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "input": "<h1>T</h1><p>a <strong>b</strong> <a href=\"https://x.com\">c</a></p><ul><li>i</li></ul>"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "HTML string to convert"
      },
      "strip_links": {
        "type": "boolean",
        "description": "Strip link URLs, keep text only (default: false)"
      }
    }
  }

  arguments 21 lines copy
• word_frequency unknown 3d ago

  Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extraction, and LLM output analysis.

  mcp-tool

  12345678910111213141516171819202122232425262728293031

  {
    "type": "object",
    "examples": [
      {
        "input": "the cat and the hat and the bat",
        "top_n": 1,
        "remove_stopwords": false
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to analyze"
      },
      "top_n": {
        "type": "number",
        "description": "Return top N words (default: 20, max: 200)"
      },
      "min_length": {
        "type": "number",
        "description": "Minimum word length to include (default: 3)"
      },
      "remove_stopwords": {
        "type": "boolean",
        "description": "Remove common English stopwords (default: true)"
      }
    }
  }

  arguments 31 lines copy
• levenshtein_distance unknown 3d ago

  Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, deduplication, and test result comparison.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738

  {
    "type": "object",
    "examples": [
      {
        "a": "kitten",
        "b": "sitting"
      }
    ],
    "properties": {
      "a": {
        "type": "string",
        "description": "First string (single-pair mode)"
      },
      "b": {
        "type": "string",
        "description": "Second string (single-pair mode)"
      },
      "batch": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "a": {
              "type": "string"
            },
            "b": {
              "type": "string"
            }
          }
        },
        "description": "Batch of {a,b} pairs (max 50)"
      },
      "case_insensitive": {
        "type": "boolean",
        "description": "Ignore case differences (default: false)"
      }
    }
  }

  arguments 38 lines copy
• hash_text unknown 34h ago

  Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or produce a fixed-length digest of any input. Supports SHA-256 (default), SHA-512, SHA-1, and MD5.

  mcp-tool

  12345678910111213141516171819202122

  {
    "type": "object",
    "examples": [
      {
        "input": "héllo wörld 🌍",
        "algorithm": "md5"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to hash"
      },
      "algorithm": {
        "type": "string",
        "description": "Hash algorithm: sha256 (default), sha512, sha1, md5"
      }
    }
  }

  arguments 22 lines copy
• base64_encode unknown 3d ago

  Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data URIs.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "héllo 🌍"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to encode"
      }
    }
  }

  arguments 17 lines copy
• llm_json_schema_check unknown 12h ago

  Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-calling and structured output testing.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445

  {
    "type": "object",
    "examples": [
      {
        "output": "{\"name\":\"a\",\"age\":3}",
        "schema": {
          "type": "object",
          "required": [
            "name",
            "age"
          ],
          "properties": {
            "age": {
              "type": "integer",
              "minimum": 0
            },
            "name": {
              "type": "string"
            },
            "role": {
              "enum": [
                "admin",
                "user"
              ]
            }
          }
        }
      }
    ],
    "required": [
      "output",
      "schema"
    ],
    "properties": {
      "output": {
        "type": "string",
        "description": "The LLM JSON output (raw string, will be parsed)"
      },
      "schema": {
        "type": "object",
        "description": "JSON Schema (draft-07 subset) to validate against",
        "additionalProperties": true
      }
    }
  }

  arguments 45 lines copy
• mcp_server_evaluate unknown 12h ago

  Check that a live MCP server over Streamable HTTP speaks the protocol, 2026-07-28 or earlier: handshake or server/discover, tools/list and its definitions, error codes, one tools/call, structuredContent against outputSchema, latency. Returns PASS/FIX/BLOCK with a 0-100 score, or INCONCLUSIVE (score null) when authorization is required. The verdict covers the PROTOCOL, never whether tools answer correctly: what_this_does_not_prove lists what was not measured. Called: only test_tool_name, or a tool annotated readOnlyHint: true. A refusal of invented arguments is not scored (tool_behaviour: refused_placeholder); pass test_tool_args to exercise the tool. Private addresses and stdio are out of reach: paste tools/list into mcp_server_health_check.

  mcp-tool

  1234567891011121314151617181920212223242526

  {
    "type": "object",
    "examples": [
      {
        "url": "https://www.ia-qa.com/mcp"
      }
    ],
    "required": [
      "url"
    ],
    "properties": {
      "url": {
        "type": "string",
        "description": "Public URL of the MCP endpoint (e.g. https://mcp.example.com/mcp). An origin alone (https://www.ia-qa.com) tries /mcp, then /."
      },
      "test_tool_args": {
        "type": "object",
        "description": "Real arguments, sent to the tested server. Without them, required ones are invented.",
        "additionalProperties": true
      },
      "test_tool_name": {
        "type": "string",
        "description": "Tool to call. Default: a read-only one, preferring no required arg."
      }
    }
  }

  arguments 26 lines copy
• metamorphic_check unknown 5h ago

  Reference-free stability check: does an assistant's answer stay the same when the QUESTION is transformed (case, typo, paraphrase, reorder, translation)? You bring the outputs; no model is called (tfidf mode is deterministic and free). Default thresholds: case .95, typo .90, paraphrase .80, reorder .80, translation .75 (embeddings only), specialization .60 (advisory, never gated). Returns PASS / FAIL / INVALID — INVALID when the BASE answer is a refusal or too short, so a model that refuses everything never scores 1.0. Pair with run_semantic_tests for correctness.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142

  {
    "type": "object",
    "examples": [
      {
        "base": {
          "output": "Paris is the capital and largest city of France."
        },
        "variants": [
          {
            "output": "Paris is the capital and largest city of France.",
            "relation": "case"
          }
        ]
      }
    ],
    "required": [
      "base",
      "variants"
    ],
    "properties": {
      "base": {
        "type": "object",
        "properties": {
          "output": {
            "type": "string",
            "description": "The answer to the original question."
          },
          "question": {
            "type": "string",
            "description": "Optional, echoed back."
          }
        },
        "description": "The original run."
      },
      "mode": {
        "enum": [
          "tfidf",
          "embeddings"
        ],
        "type": "string",
        "description": "tfidf (default, lexical: gate on case/typo, read paraphrase as a trend) or embeddings (OpenAI, needs api_key; required for translation)."
      },
      "api_key": {
        "type": "string",
        "description": "OpenAI key, embeddings mode only."
      },
      "variants": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "relation",
            "output"
          ],
          "properties": {
            "id": {
              "type": "string"
            },
            "output": {
              "type": "string",
              "description": "The answer to the transformed question."
            },
            "question": {
              "type": "string",
              "description": "Optional, echoed back."
            },
            "relation": {
              "enum": [
                "case",
                "typo",
                "paraphrase",
                "reorder",
                "translation",
                "specialization"
              ],
              "type": "string",
              "description": "Transformation applied to the question; sets the metric and threshold."
            }
          }
        },
        "maxItems": 20,
        "description": "Answers to transformed versions of the question."
      },
      "thresholds": {
        "type": "object",
        "properties": {
          "case": {
            "type": "number",
            "maximum": 1,
            "minimum": 0
          },
          "typo": {
            "type": "number",
            "maximum": 1,
            "minimum": 0
          },
          "reorder": {
            "type": "number",
            "maximum": 1,
            "minimum": 0
          },
          "paraphrase": {
            "type": "number",
            "maximum": 1,
            "minimum": 0
          },
          "translation": {
            "type": "number",
            "maximum": 1,
            "minimum": 0
          },
          "specialization": {
            "type": "number",
            "maximum": 1,
            "minimum": 0
          }
        },
        "description": "Per-relation overrides, 0-1. Calibrate on your own data before gating."
      },
      "require_all": {
        "type": "boolean",
        "description": "Default true: every gated variant must pass. Keep it for a gate — false lets one easy typo row hold the run at PASS while a paraphrase fails."
      },
      "baseline_guard": {
        "type": "object",
        "properties": {
          "min_length": {
            "type": "number",
            "description": "Default 40 chars."
          },
          "must_not_match": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "description": "Refusal patterns, checked in the first 200 chars. Replaces the default FR+EN list; [] disables."
          }
        },
        "description": "Floor on the BASE answer; failing it returns INVALID."
      }
    }
  }

  arguments 142 lines copy
• minify_js unknown 5h ago

  Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML templates, report payloads, or injecting inline code programmatically.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "code": "function add(a, b) {\n  return a + b;\n}"
      }
    ],
    "required": [
      "code"
    ],
    "properties": {
      "code": {
        "type": "string",
        "description": "JavaScript code to minify (max 50kb)"
      }
    }
  }

  arguments 17 lines copy
• mock_from_schema unknown 5h ago

  Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, date-time, uri, uuid), enum, const, and nested schemas.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253

  {
    "type": "object",
    "examples": [
      {
        "count": 20,
        "schema": {
          "type": "object",
          "required": [
            "id",
            "email"
          ],
          "properties": {
            "id": {
              "type": "integer",
              "maximum": 9,
              "minimum": 5
            },
            "tag": {
              "enum": [
                "a",
                "b"
              ]
            },
            "email": {
              "type": "string",
              "format": "email"
            }
          }
        }
      }
    ],
    "required": [
      "schema"
    ],
    "properties": {
      "seed": {
        "type": "string",
        "description": "Optional seed string for deterministic output (uses first char codes)"
      },
      "count": {
        "type": "number",
        "description": "Number of mock objects to generate (default: 1, max: 20)"
      },
      "schema": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The JSON Schema to generate from — a JSON string, or the schema object itself."
      }
    }
  }

  arguments 53 lines copy
• generate_html_report unknown 37h ago

  Convert a run_eval_contract() LLM Test Runner JSON result into a fully self-contained dark-themed HTML report with Pass/Fail badges, side-by-side Input/Output/Ground-Truth panels, evaluator score bars, and a radar chart. Returns the HTML as a string.

  mcp-tool

  123456789101112131415161718

  {
    "type": "object",
    "examples": [
      {
        "results": {}
      }
    ],
    "required": [
      "results"
    ],
    "properties": {
      "results": {
        "type": "object",
        "description": "The JSON object returned by run_eval_contract()",
        "additionalProperties": true
      }
    }
  }

  arguments 18 lines copy
• compare_models unknown 3d ago

  Put 2-5 AI models side by side: price per 1k tokens, context window, multimodal, reasoning, provider, pricing page. Facts only: names the cheapest and the largest context, never a "best model" — run your cases on each to choose.

  mcp-tool

  1234567891011121314151617181920212223

  {
    "type": "object",
    "examples": [
      {
        "models": [
          "gpt-6.1-sol",
          "claude-sonnet-5-5"
        ]
      }
    ],
    "required": [
      "models"
    ],
    "properties": {
      "models": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Array of 2-5 model names (e.g. [\"gpt-5\",\"claude-sonnet-4-6\",\"gemini-3.5-flash\"])"
      }
    }
  }

  arguments 23 lines copy
• calculate_readability unknown 3d ago

  Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM output quality.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "The cat sat on the mat. The dog ran."
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to analyze for readability"
      }
    }
  }

  arguments 17 lines copy
• detect_language unknown 2d ago

  Detect the natural language of a text using n-gram frequency analysis and common word markers. Supports 15 languages: English, French, Spanish, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, Polish, Turkish, Swedish.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "Il rilascio è fallito perché la migrazione del database è scaduta dopo dieci minuti."
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to detect language from (min 20 chars for accuracy)"
      }
    }
  }

  arguments 17 lines copy
• url_encode unknown 4d ago

  Percent-encode a string for safe use in query strings, path segments or form-encoded bodies, so it cannot inject or break a URL.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "input": "a b&c=d/é"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "mode": {
        "type": "string",
        "description": "\"component\" (default) or \"full\" for encodeURI behavior"
      },
      "input": {
        "type": "string",
        "description": "String to URL-encode"
      }
    }
  }

  arguments 21 lines copy
• url_decode unknown 4d ago

  Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "a%20b%26c"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "URL-encoded string to decode"
      }
    }
  }

  arguments 17 lines copy
• escape_html unknown 2d ago

  Escape HTML special characters (&, <, >, ", ') to their safe HTML entities. Escaped text inserted into HTML cannot run as script (XSS).

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "<a href=\"x\">'&'</a>"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "String to HTML-escape"
      }
    }
  }

  arguments 17 lines copy
• find_tool unknown 2d ago

  Search available MCP tools by keyword or category before calling them. Returns names, descriptions and, on request, inputSchemas. Queries are logged 90 days, screened for credentials and personal data. Categories: data, dev, encoding, eval, integrations, llm, meta, qa, rag, security, text, web.

  mcp-tool

  1234567891011121314151617181920212223242526272829

  {
    "type": "object",
    "examples": [
      {
        "query": "leaked api key"
      }
    ],
    "required": [
      "query"
    ],
    "properties": {
      "query": {
        "type": "string",
        "description": "Keywords, e.g. \"cors\", \"token\", \"vector\", \"json\""
      },
      "category": {
        "type": "string",
        "description": "Optional filter, one of the categories above. Any other value is refused."
      },
      "max_results": {
        "type": "number",
        "description": "Maximum tools to return (default 10, max 50). Best match first."
      },
      "with_schema": {
        "type": "boolean",
        "description": "Set true to include inputSchema in results (default: false)"
      }
    }
  }

  arguments 29 lines copy
• run_pr_gate_pipeline unknown never probed

  Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impact areas, a risk score 0–100 with the factors that built it (churn, files touched, sensitive paths, whether any test file changed, lint severities, impacted risk areas), generated test cases, and a PASS / CONDITIONAL / BLOCK recommendation. Advisory: the score measures properties of the diff, not the correctness of the change — it does not read the code semantically and does not replace a reviewer or a static analyser. See notAnalysed in the response.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "git_diff": "diff --git a/pay.js b/pay.js\n--- a/pay.js\n+++ b/pay.js\n@@ -1 +1 @@\n-if (amount > 0) charge(amount);\n+if (amount >= 0) charge(amount);"
      }
    ],
    "required": [
      "git_diff"
    ],
    "properties": {
      "context": {
        "type": "string",
        "description": "Optional PR title or description for richer analysis"
      },
      "git_diff": {
        "type": "string",
        "description": "Unified git diff (output of `git diff HEAD` or copied from GitHub diff view)"
      }
    }
  }

  arguments 21 lines copy
• run_eval_contract unknown never probed

  Run an IA-EVAL test suite (.ia-eval.yaml contract): call the model per scenario; return passed (true, false, null if undecided) and a verdict (PASS, FAIL, PARTIAL). Expectations are assertions that fail a scenario whatever its score; evaluators give the score. Hosted: pass inline_contract. validate_only calls no model.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889

  {
    "type": "object",
    "examples": [
      {
        "inline_contract": {
          "metadata": {
            "name": "smoke",
            "version": "1.0"
          },
          "scenarios": [
            {
              "id": "capital",
              "input": "Capital of France? One word.",
              "ground_truth": "Paris"
            }
          ],
          "expectations": {
            "contains": [
              "Paris"
            ],
            "min_score": 70
          }
        }
      }
    ],
    "properties": {
      "api_keys": {
        "type": "object",
        "properties": {
          "hf": {
            "type": "string"
          },
          "xai": {
            "type": "string"
          },
          "groq": {
            "type": "string"
          },
          "google": {
            "type": "string"
          },
          "openai": {
            "type": "string"
          },
          "mistral": {
            "type": "string"
          },
          "deepseek": {
            "type": "string"
          },
          "anthropic": {
            "type": "string"
          }
        },
        "description": "Your keys per provider. Only Groq falls back to the server key; any other provider needs yours."
      },
      "overrides": {
        "type": "object",
        "properties": {
          "model": {
            "type": "string"
          },
          "provider": {
            "type": "string"
          },
          "temperature": {
            "type": "number"
          },
          "system_prompt": {
            "type": "string"
          }
        },
        "description": "Override contract defaults"
      },
      "contract_path": {
        "type": "string",
        "description": "A .ia-eval.yaml under the server's working directory (local use)."
      },
      "validate_only": {
        "type": "boolean",
        "description": "Check the contract against the IA-EVAL schema and stop: no model call, no key, deterministic. Returns valid + issues instead of throwing. Gate a PR that edits a contract on valid."
      },
      "inline_contract": {
        "type": "object",
        "description": "The contract as an object: \"metadata\" ({name, version, model?, provider?}), \"expectations\", \"evaluators\" and \"scenarios\" ([{id, input, expectations?}]). generate_eval_yaml scaffolds one.",
        "additionalProperties": true
      }
    }
  }

  arguments 89 lines copy
• run_semantic_tests reads unknown never probed

  Semantic assertion primitive: compare actual vs expected text pairs using cosine similarity + ROUGE-L. Two modes: tfidf (default, free, no API key) or embeddings (OpenAI text-embedding-3-small, BYOK, true semantic similarity). Returns per-case PASS/FAIL verdicts and an overall verdict. CI-ready: pipe the JSON verdict field to gate a build.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778

  {
    "type": "object",
    "examples": [
      {
        "cases": [
          {
            "actual": "Paris is the capital of France",
            "expected": "The capital of France is Paris"
          }
        ]
      }
    ],
    "required": [
      "cases"
    ],
    "properties": {
      "mode": {
        "enum": [
          "tfidf",
          "embeddings"
        ],
        "type": "string",
        "description": "tfidf (default): fast, free, lexical. embeddings: OpenAI text-embedding-3-small, true semantic similarity, requires api_key."
      },
      "cases": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "actual",
            "expected"
          ],
          "properties": {
            "id": {
              "type": "string",
              "description": "Optional identifier for this case."
            },
            "actual": {
              "type": "string",
              "description": "The text produced by your LLM/system."
            },
            "expected": {
              "type": "string",
              "description": "The reference/ground-truth text."
            }
          }
        },
        "maxItems": 50,
        "description": "Array of (actual, expected) pairs to evaluate."
      },
      "api_key": {
        "type": "string",
        "description": "OpenAI API key — required only when mode is embeddings."
      },
      "thresholds": {
        "type": "object",
        "properties": {
          "cosine": {
            "type": "number",
            "maximum": 1,
            "minimum": 0,
            "description": "Minimum cosine similarity to pass (default: 0.75)."
          },
          "rouge_l": {
            "type": "number",
            "maximum": 1,
            "minimum": 0,
            "description": "Minimum ROUGE-L F1 to pass (default: 0.5)."
          }
        },
        "description": "Pass/fail thresholds (defaults: cosine 0.75, rouge_l 0.5)."
      },
      "require_all": {
        "type": "boolean",
        "description": "If true (default), all cases must pass for overall PASS. If false, at least one case passing returns PASS."
      }
    }
  }

  arguments 78 lines copy
• diff_text reads unknown 2d ago

  Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configurable context lines (0–20). Use to compare versions of prompts, configs, code snippets, or any text where you need to see exactly what changed.

  mcp-tool

  123456789101112131415161718192021222324252627

  {
    "type": "object",
    "examples": [
      {
        "a": "a\nb\nc",
        "b": "a\nB\nc"
      }
    ],
    "required": [
      "a",
      "b"
    ],
    "properties": {
      "a": {
        "type": "string",
        "description": "Original (before) text"
      },
      "b": {
        "type": "string",
        "description": "Modified (after) text"
      },
      "context": {
        "type": "number",
        "description": "Context lines around each change (0–20, default: 3)"
      }
    }
  }

  arguments 27 lines copy
• function_call_validate unknown 44h ago

  Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema (types, enums, ranges), no extra/missing args. Reads the definitions as OpenAI (`parameters`), MCP (`inputSchema`) or Anthropic (`input_schema`) write them, or as plain `required_args`/`optional_args`; reads the call as `arguments` (object or JSON string), `input` (Anthropic tool_use) or an OpenAI `{function:{…}}` wrapper.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071

  {
    "type": "object",
    "examples": [
      {
        "function_call": {
          "name": "get_weather",
          "arguments": {
            "city": "Paris"
          }
        },
        "allowed_functions": [
          {
            "name": "get_weather",
            "required_args": [
              "city"
            ]
          }
        ]
      }
    ],
    "required": [
      "function_call",
      "allowed_functions"
    ],
    "properties": {
      "function_call": {
        "type": "object",
        "description": "The function call object from LLM (e.g. { \"name\": \"get_weather\", \"arguments\": {\"city\":\"Paris\"} })",
        "additionalProperties": true
      },
      "allowed_functions": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "name": {
              "type": "string"
            },
            "parameters": {
              "type": "object",
              "description": "OpenAI form: a JSON Schema of the arguments",
              "additionalProperties": true
            },
            "inputSchema": {
              "type": "object",
              "description": "MCP form",
              "additionalProperties": true
            },
            "input_schema": {
              "type": "object",
              "description": "Anthropic form",
              "additionalProperties": true
            },
            "optional_args": {
              "type": "array",
              "items": {
                "type": "string"
              }
            },
            "required_args": {
              "type": "array",
              "items": {
                "type": "string"
              }
            }
          }
        },
        "description": "List of allowed function definitions, in OpenAI, MCP or Anthropic form, or as {name, required_args, optional_args}"
      }
    }
  }

  arguments 71 lines copy
• mcp_schema_lint unknown 12h ago

  Lint one MCP tool definition with the same rules as mcp_server_health_check: errors break the MCP spec (FAIL), documentation_issues are what its strict mode refuses — a parameter without description or type (FIX, never an A) — and suggestions are style only (snake_case) and change nothing.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233

  {
    "type": "object",
    "examples": [
      {
        "tool_definition": {
          "name": "get_weather",
          "description": "Return the current weather for a city. Use it when the user asks about the weather now.",
          "inputSchema": {
            "type": "object",
            "required": [
              "city"
            ],
            "properties": {
              "city": {
                "type": "string",
                "description": "City name, e.g. Paris"
              }
            }
          }
        }
      }
    ],
    "required": [
      "tool_definition"
    ],
    "properties": {
      "tool_definition": {
        "type": "object",
        "description": "MCP tool definition object with name, description, inputSchema",
        "additionalProperties": true
      }
    }
  }

  arguments 33 lines copy
• ab_test_report reads unknown 3d ago

  Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns mean, median, std deviation, improvement percentage, and a Welch t-test at 95 %: `winner` names a variant only when the difference is significant — otherwise "no_significant_difference", or "insufficient_samples" under 2 scores per variant.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263

  {
    "type": "object",
    "examples": [
      {
        "variant_a": {
          "name": "A",
          "scores": [
            70,
            71,
            69
          ]
        },
        "variant_b": {
          "name": "B",
          "scores": [
            70,
            71,
            69
          ]
        }
      }
    ],
    "required": [
      "variant_a",
      "variant_b"
    ],
    "properties": {
      "variant_a": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "description": "Name/label for variant A"
          },
          "scores": {
            "type": "array",
            "items": {
              "type": "number"
            },
            "description": "Array of scores (0-100)"
          }
        },
        "description": "First variant configuration with name and score array"
      },
      "variant_b": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "description": "Name/label for variant B"
          },
          "scores": {
            "type": "array",
            "items": {
              "type": "number"
            },
            "description": "Array of scores (0-100)"
          }
        },
        "description": "Second variant configuration with name and score array"
      }
    }
  }

  arguments 63 lines copy
• shield_analyze unknown 5d ago

  One-call safety gate for an LLM response: grounding (with source), prompt injection on the response AND the prompt (checks.injection / checks.injection_prompt), toxicity, PII, guardrail rules, quality, plus an LLM review on Groq (server key; model "none" skips it and sends nothing out). Returns PASS/FIX/BLOCK, a 0-100 safety score, per-check results and fixes. PII gates the verdict: SSN or card → BLOCK, email → FIX, phone/IP flagged only.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849

  {
    "type": "object",
    "examples": [
      {
        "model": "none",
        "prompt": "When will my order arrive?",
        "response": "Your order #1234 will arrive tomorrow."
      }
    ],
    "required": [
      "response"
    ],
    "properties": {
      "model": {
        "type": "string",
        "description": "LLM review model: openai/gpt-oss-20b, openai/gpt-oss-120b, qwen/qwen3.8-27b (default openai/gpt-oss-20b), or \"none\"."
      },
      "rules": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "type": {
              "type": "string"
            },
            "label": {
              "type": "string"
            },
            "value": {
              "type": "string"
            }
          }
        },
        "description": "Same format as guardrail_test."
      },
      "prompt": {
        "type": "string",
        "description": "The original prompt — also scanned for injection; pass it whenever you have it."
      },
      "source": {
        "type": "string",
        "description": "Reference text for the grounding check."
      },
      "response": {
        "type": "string",
        "description": "The LLM response to check."
      }
    }
  }

  arguments 49 lines copy
• cookie_security_audit unknown never probed

  Audit the security attributes of cookies set by any URL. Fetches the URL and inspects all Set-Cookie headers for: HttpOnly, Secure, SameSite, Domain scope, Path scope, Max-Age/Expires, __Host-/__Secure- prefixes. Flags insecure patterns: missing HttpOnly on session cookies, missing Secure flag, SameSite=None without Secure, overly broad Domain, and excessive TTL. Returns per-cookie grades and an overall security score (0–100) — the score is the WEAKEST cookie, not the average, so one leaking session cookie cannot be averaged into a green result (average_score is reported separately).

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "url": "https://www.ia-qa.com"
      }
    ],
    "required": [
      "url"
    ],
    "properties": {
      "url": {
        "type": "string",
        "description": "Full URL to audit (e.g. https://example.com/login)"
      }
    }
  }

  arguments 17 lines copy
• embedding_similarity unknown 5d ago

  Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: true vector embeddings are run_semantic_tests with mode="embeddings" and an OpenAI key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950

  {
    "type": "object",
    "examples": [
      {
        "text_a": "the cat sat",
        "text_b": "quantum field theory"
      }
    ],
    "properties": {
      "batch": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "text_a",
            "text_b"
          ],
          "properties": {
            "text_a": {
              "type": "string"
            },
            "text_b": {
              "type": "string"
            }
          }
        },
        "description": "Batch mode: array of { text_a, text_b } pairs. Overrides text_a/text_b if provided."
      },
      "text_a": {
        "type": "string",
        "description": "First text to compare (single-pair mode)"
      },
      "text_b": {
        "type": "string",
        "description": "Second text to compare (single-pair mode)"
      },
      "methods": {
        "type": "array",
        "items": {
          "enum": [
            "bow",
            "tfidf",
            "ngram"
          ],
          "type": "string"
        },
        "description": "Algorithms to use (default: all three). Options: \"bow\", \"tfidf\", \"ngram\""
      }
    }
  }

  arguments 50 lines copy
• few_shot_formatter unknown 2d ago

  Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758

  {
    "type": "object",
    "examples": [
      {
        "examples": [
          {
            "input": "a",
            "output": "b"
          },
          {
            "input": "c",
            "output": "d"
          }
        ]
      }
    ],
    "required": [
      "examples"
    ],
    "properties": {
      "format": {
        "enum": [
          "chat",
          "xml",
          "markdown",
          "plain"
        ],
        "type": "string",
        "description": "Output format (default: chat)"
      },
      "examples": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "input": {
              "type": "string"
            },
            "label": {
              "type": "string"
            },
            "output": {
              "type": "string"
            }
          }
        },
        "description": "Array of {input, output} pairs"
      },
      "input_label": {
        "type": "string",
        "description": "Label for input (default: User / <input>)"
      },
      "output_label": {
        "type": "string",
        "description": "Label for output (default: Assistant / <output>)"
      }
    }
  }

  arguments 58 lines copy
• extract_json_from_text unknown 2d ago

  Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks and inline JSON.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "Here:\n```json\n{\"a\":[1,2]}\n```\nthanks"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Raw text (e.g., LLM output) that may contain a JSON object or array"
      }
    }
  }

  arguments 17 lines copy
• model_info reads unknown 1h ago

  Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature list. Covers 30+ models from OpenAI, Anthropic, Google, DeepSeek, Meta, Mistral, Cohere, xAI.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "model": "gpt-4o"
      }
    ],
    "required": [
      "model"
    ],
    "properties": {
      "model": {
        "type": "string",
        "description": "Model name (e.g. \"gpt-4o\", \"claude-3.5-sonnet\", \"gemini-3.1-pro-preview\")"
      }
    }
  }

  arguments 17 lines copy
• llm_generate unknown 16h ago

  Generate text with an open-source model hosted on Groq, on the server's key — no API key needed. Models: openai/gpt-oss-20b, openai/gpt-oss-120b, qwen/qwen3.8-27b (default "openai/gpt-oss-20b"). HuggingFace models are not served on the server key. A reasoning model that spends max_tokens thinking is retried once at 4096. Rate-limited.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233

  {
    "type": "object",
    "examples": [
      {
        "prompt": "Reply with the single word OK."
      }
    ],
    "required": [
      "prompt"
    ],
    "properties": {
      "model": {
        "type": "string",
        "description": "Model ID (default: \"openai/gpt-oss-20b\"). Server-keyed whitelist only — Groq: openai/gpt-oss-20b, openai/gpt-oss-120b, qwen/qwen3.8-27b. Other ids from list_llm_models, HuggingFace ones included, are BYOK-only and will be rejected."
      },
      "prompt": {
        "type": "string",
        "description": "The user prompt / instruction to send to the model"
      },
      "system": {
        "type": "string",
        "description": "Optional system prompt to set context or persona"
      },
      "max_tokens": {
        "type": "number",
        "description": "Maximum tokens to generate (default: 2048, max: 4096)"
      },
      "temperature": {
        "type": "number",
        "description": "Sampling temperature 0.0–1.5 (default: 0.7)"
      }
    }
  }

  arguments 33 lines copy
• create_confluence_page unknown never probed

  Create a new Confluence page from the output of jira_to_test_suite. Formats Gherkin, E2E steps, API tests, and test data as a properly structured Confluence page with code blocks and tables. STATEFUL — creates a new page in the specified space.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162

  {
    "type": "object",
    "examples": [
      {
        "space_key": "QA",
        "test_suite": {
          "acceptance_criteria": [
            "A reset link expires 30 minutes after it is sent."
          ]
        },
        "confluence_email": "[email protected]",
        "confluence_token": "<your-atlassian-token>",
        "confluence_base_url": "https://yourcompany.atlassian.net"
      }
    ],
    "required": [
      "confluence_base_url",
      "confluence_email",
      "confluence_token",
      "space_key",
      "test_suite"
    ],
    "properties": {
      "title": {
        "type": "string",
        "description": "Page title. Defaults to \"Test Plan: {issue_key}\""
      },
      "issue_key": {
        "type": "string",
        "description": "Source Jira issue key (for the page title and source link)"
      },
      "issue_url": {
        "type": "string",
        "description": "Source Jira issue URL (added as a link in the page)"
      },
      "space_key": {
        "type": "string",
        "description": "Confluence space key where the page will be created, e.g. \"QA\", \"ENG\""
      },
      "test_suite": {
        "type": "object",
        "description": "The test_suite object from jira_to_test_suite result",
        "additionalProperties": true
      },
      "parent_page_id": {
        "type": "string",
        "description": "Optional parent page ID — page will be created as a child of this page"
      },
      "confluence_email": {
        "type": "string",
        "description": "Atlassian account email"
      },
      "confluence_token": {
        "type": "string",
        "description": "Atlassian API token"
      },
      "confluence_base_url": {
        "type": "string",
        "description": "Atlassian base URL"
      }
    }
  }

  arguments 62 lines copy
• fetch_confluence_page unknown 2d ago

  Fetch a Confluence page and return its content as clean Markdown. Accepts a numeric page_id or a full page URL. Optionally lists direct child pages. BYOK — credentials transit in-memory only, never stored.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142

  {
    "type": "object",
    "examples": [
      {
        "page_id": "123456",
        "confluence_email": "[email protected]",
        "confluence_token": "<your-atlassian-token>",
        "confluence_base_url": "https://yourcompany.atlassian.net"
      }
    ],
    "required": [
      "confluence_base_url",
      "confluence_email",
      "confluence_token"
    ],
    "properties": {
      "page_id": {
        "type": "string",
        "description": "Confluence page ID (numeric string), e.g. \"123456789\""
      },
      "page_url": {
        "type": "string",
        "description": "Full Confluence page URL (alternative to page_id), e.g. \"https://mycompany.atlassian.net/wiki/spaces/ENG/pages/123456789\""
      },
      "confluence_email": {
        "type": "string",
        "description": "Atlassian account email (same credentials as Jira)"
      },
      "confluence_token": {
        "type": "string",
        "description": "Atlassian API token"
      },
      "include_children": {
        "type": "boolean",
        "description": "List direct child pages (id + title) (default: false)"
      },
      "confluence_base_url": {
        "type": "string",
        "description": "Atlassian base URL, e.g. \"https://mycompany.atlassian.net\""
      }
    }
  }

  arguments 42 lines copy
• check_contrast_ratio unknown 3d ago

  Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text.

  mcp-tool

  1234567891011121314151617181920212223

  {
    "type": "object",
    "examples": [
      {
        "background": "#fff",
        "foreground": "#000"
      }
    ],
    "required": [
      "foreground",
      "background"
    ],
    "properties": {
      "background": {
        "type": "string",
        "description": "Background color in hex (e.g., \"#ffffff\")"
      },
      "foreground": {
        "type": "string",
        "description": "Foreground color in hex (e.g., \"#333333\")"
      }
    }
  }

  arguments 23 lines copy
• cron_parse unknown 3d ago

  Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday).

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "expression": "*/5 * * * *"
      }
    ],
    "required": [
      "expression"
    ],
    "properties": {
      "expression": {
        "type": "string",
        "description": "Cron expression (e.g., \"0 9 * * 1-5\", \"*/15 * * * *\")"
      }
    }
  }

  arguments 17 lines copy
• parse_http_headers unknown 6d ago

  Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing security headers (HSTS, CSP, X-Frame-Options, etc.).

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "headers": "Content-Type: text/html\nSet-Cookie: a=1\nSet-Cookie: b=2"
      }
    ],
    "required": [
      "headers"
    ],
    "properties": {
      "headers": {
        "type": "string",
        "description": "Raw HTTP headers (one \"Name: Value\" per line)"
      },
      "analyze_security": {
        "type": "boolean",
        "description": "Audit for missing security headers (default: true)"
      }
    }
  }

  arguments 21 lines copy
• count_code_lines unknown 3d ago

  Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS.

  mcp-tool

  12345678910111213141516171819202122

  {
    "type": "object",
    "examples": [
      {
        "input": "// c\nconst a = 1; // x\n\n/* b\n c */\nlet b;",
        "language": "javascript"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Source code to analyze"
      },
      "language": {
        "type": "string",
        "description": "Language hint: \"js\", \"ts\", \"py\", \"java\", \"c\", \"rb\", \"go\", \"sh\", \"html\", \"css\" (auto-detect if omitted)"
      }
    }
  }

  arguments 22 lines copy
• format_bytes unknown 3d ago

  Convert raw byte counts to human-readable sizes in SI (KB=1000) or IEC (KiB=1024) units, or parse size strings back to bytes. Covers B, KB/KiB, MB/MiB, GB/GiB, TB/TiB, PB/PiB.

  mcp-tool

  12345678910111213141516171819202122232425262728

  {
    "type": "object",
    "examples": [
      {
        "bytes": 1536,
        "standard": "iec"
      }
    ],
    "properties": {
      "bytes": {
        "type": "number",
        "description": "Number of bytes to format"
      },
      "standard": {
        "enum": [
          "both",
          "si",
          "iec"
        ],
        "type": "string",
        "description": "Output standard (default: both)"
      },
      "size_string": {
        "type": "string",
        "description": "Size string to parse to bytes (e.g. \"1.5 GB\", \"512 MiB\")"
      }
    }
  }

  arguments 28 lines copy
• openapi_validate unknown 6d ago

  Validate the structure of an OpenAPI 3.x specification (JSON or YAML). Checks required top-level fields (openapi, info.title, info.version, paths), validates each operation (responses, operationId uniqueness), detects undeclared $ref components, and flags missing 2xx responses. Returns a PASS/FAIL verdict, a 0–100 compliance score, and a list of errors and warnings with JSON-pointer locations. Use before publishing an API spec or generating SDK code.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839

  {
    "type": "object",
    "examples": [
      {
        "input": {
          "info": {
            "title": "t",
            "version": "1"
          },
          "paths": {
            "/a": {
              "get": {
                "responses": {
                  "200": {
                    "description": "ok"
                  }
                },
                "operationId": "getA"
              }
            }
          },
          "openapi": "3.0.3"
        }
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The OpenAPI 3.x spec — a JSON string, a YAML string, or the already-parsed spec object."
      }
    }
  }

  arguments 39 lines copy
• yaml_to_json unknown 3d ago

  Parse a YAML string and return the equivalent JSON value. The reverse of json_to_yaml. Supports nested objects, arrays, anchors, aliases, multi-document streams, and all scalar types. Use when processing config files, CI/CD pipeline definitions, or OpenAPI specs authored in YAML.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "input": "a: 1\nb: [x, y]"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "YAML string to parse"
      },
      "multi": {
        "type": "boolean",
        "description": "If true, parse all documents in a multi-document stream and return an array (default: false)"
      }
    }
  }

  arguments 21 lines copy
• json_schema_generate unknown 27h ago

  Infer a JSON Schema (draft-07) from a sample JSON value. Detects types, required fields, array item shapes, nested objects, and common string formats (email, uri, date, date-time, uuid). Returns a ready-to-use schema compatible with json_schema_validate. Use when you have a sample API response or LLM output and want to auto-generate a validation schema for CI/CD testing.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243

  {
    "type": "object",
    "examples": [
      {
        "input": {
          "a": [
            1
          ],
          "b": true,
          "f": 1.5,
          "n": 1,
          "o": {
            "k": null
          },
          "s": "x"
        }
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The sample JSON value to infer the schema from — a JSON string, or the value itself."
      },
      "samples": {
        "type": [
          "array",
          "string"
        ],
        "description": "More samples of the same shape, merged with input: a field seen as null and as a date becomes [\"string\",\"null\"]; one missing somewhere is not required."
      },
      "required_all": {
        "type": "boolean",
        "description": "Mark fields present in every observed object as required (default: true)"
      }
    }
  }

  arguments 43 lines copy
• regex_test unknown 6d ago

  Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user inputs, extracting structured data from text, or debugging regex patterns. Supports flags g, i, m, s, u, y. The match runs in an isolated thread with a 500 ms budget: a pattern that blows up (catastrophic backtracking, e.g. "(a+)+$") comes back as redos_detected:true — a real ReDoS verdict on your pattern — instead of hanging.

  mcp-tool

  12345678910111213141516171819202122232425262728

  {
    "type": "object",
    "examples": [
      {
        "flags": "g",
        "input": "a 12-34 b 5-6",
        "pattern": "\\d+-\\d+"
      }
    ],
    "required": [
      "pattern",
      "input"
    ],
    "properties": {
      "flags": {
        "type": "string",
        "description": "Regex flags: g (global), i (case-insensitive), m (multiline), s (dotAll) — default: \"\""
      },
      "input": {
        "type": "string",
        "description": "The string to test against (max 50 KB)"
      },
      "pattern": {
        "type": "string",
        "description": "Regular expression pattern (without delimiters)"
      }
    }
  }

  arguments 28 lines copy
• strip_markdown unknown 5d ago

  Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped documentation, README files, or user content into an LLM prompt to eliminate redundant markup tokens and reduce cost.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "# Title\n**bold** and [link](http://x.com) and `code`\n- item"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Markdown text to convert to plain text"
      }
    }
  }

  arguments 17 lines copy
• extract_links unknown 2d ago

  Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web scraping validation.

  mcp-tool

  1234567891011121314151617181920212223242526272829

  {
    "type": "object",
    "examples": [
      {
        "input": "nothing here"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to extract links from"
      },
      "types": {
        "type": "array",
        "items": {
          "enum": [
            "url",
            "email",
            "domain"
          ],
          "type": "string"
        },
        "description": "Types to extract (default: all three)"
      }
    }
  }

  arguments 29 lines copy
• generate_password unknown never probed

  Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when resetting user passwords, seeding test accounts, or generating API secrets.

  mcp-tool

  1234567891011121314151617181920212223242526272829

  {
    "type": "object",
    "examples": [
      {
        "length": 24,
        "numbers": true,
        "symbols": true,
        "uppercase": true
      }
    ],
    "properties": {
      "length": {
        "type": "number",
        "description": "Password length (4–128, default: 16)"
      },
      "numbers": {
        "type": "boolean",
        "description": "Include digits (default: true)"
      },
      "symbols": {
        "type": "boolean",
        "description": "Include symbols like !@#$ (default: false)"
      },
      "uppercase": {
        "type": "boolean",
        "description": "Include uppercase letters (default: true)"
      }
    }
  }

  arguments 29 lines copy
• number_base_convert unknown 7d ago

  Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes.

  mcp-tool

  123456789101112131415161718192021222324252627

  {
    "type": "object",
    "examples": [
      {
        "input": "ff",
        "to_base": 10,
        "from_base": 16
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Number to convert (e.g., \"255\", \"0xFF\", \"0b1010\", \"0o77\")"
      },
      "to_base": {
        "type": "number",
        "description": "Target base 2–36 (omit to get all common bases)"
      },
      "from_base": {
        "type": "number",
        "description": "Source base 2–36 (auto-detects prefix if omitted)"
      }
    }
  }

  arguments 27 lines copy
• needle_haystack_generate unknown 1h ago

  Generate a "needle in a haystack" test: embeds a target fact into a large block of filler text at a specified position. Use this to test LLM context window retrieval accuracy. Returns the full haystack, the question to ask, and metadata. No API key needed.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041

  {
    "type": "object",
    "examples": [
      {
        "needle": "The code is 4242.",
        "tokens": 2000,
        "position": "middle",
        "question": "What is the code?"
      }
    ],
    "required": [
      "needle",
      "question"
    ],
    "properties": {
      "needle": {
        "type": "string",
        "description": "The fact to hide (e.g. \"The secret code is ALPHA-42\")"
      },
      "tokens": {
        "type": "integer",
        "default": 5000,
        "description": "Target haystack size in tokens (default: 5000, max: 100000)"
      },
      "position": {
        "enum": [
          "start",
          "middle",
          "end",
          "random"
        ],
        "type": "string",
        "default": "middle",
        "description": "Where to insert the needle: \"start\", \"middle\", \"end\", \"random\" (default: \"middle\")"
      },
      "question": {
        "type": "string",
        "description": "The question to ask the LLM (e.g. \"What is the secret code?\")"
      }
    }
  }

  arguments 41 lines copy
• normalize_whitespace reads unknown 1h ago

  Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, and text before processing.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445

  {
    "type": "object",
    "examples": [
      {
        "input": "a  \r\nb\t\r\n\r\n\r\n\r\nc",
        "trim_lines": true,
        "line_ending": "lf",
        "collapse_blanks": true,
        "max_blank_lines": 1
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Text to normalize"
      },
      "trim_file": {
        "type": "boolean",
        "description": "Trim leading/trailing blank lines (default: true)"
      },
      "trim_lines": {
        "type": "boolean",
        "description": "Trim trailing whitespace from each line (default: true)"
      },
      "line_ending": {
        "type": "string",
        "description": "\"lf\" (default), \"crlf\", or \"cr\""
      },
      "tab_to_spaces": {
        "type": "number",
        "description": "Convert tabs to N spaces (omit to keep tabs)"
      },
      "collapse_blanks": {
        "type": "boolean",
        "description": "Collapse runs of blank lines down to max_blank_lines (default: true)"
      },
      "max_blank_lines": {
        "type": "number",
        "description": "Blank lines to keep when collapsing, 0-10 (default: 2)"
      }
    }
  }

  arguments 45 lines copy
• json_diff unknown 27h ago

  Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects, for API response regression testing. Arrays are not compared blindly by position: the same elements in a different order collapse to a single "reordered" change, and an array of records sharing a stable identity field (id, uuid, key, name…) is matched by that field, so paths read [id=42] and a moved record is not reported as N rewrites.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738394041

  {
    "type": "object",
    "examples": [
      {
        "after": {
          "a": 1,
          "b": 2
        },
        "before": {
          "a": 1,
          "b": 2
        }
      }
    ],
    "required": [
      "before",
      "after"
    ],
    "properties": {
      "after": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The modified JSON (after) — a JSON string, or the value itself."
      },
      "before": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The original JSON (before) — a JSON string, or the value itself."
      },
      "max_depth": {
        "type": "number",
        "description": "Max nesting depth to recurse (default: 10)"
      }
    }
  }

  arguments 41 lines copy
• run_vlm_test_suite unknown never probed

  Test a vision model (OpenAI, Anthropic or Gemini) on one image: each test case asks a question and asserts on the answer (contains, not_contains, json_format, min_length, max_length, semantic_contains = TF-IDF cosine ≥ 0.4). Returns per-case PASS/FAIL, the pass rate, `overall` PASS/WARNING/FAIL against threshold (N/A when no assertion could run), and latency. BYOK: your key for the model's provider; the image is fetched only from a public address.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990

  {
    "type": "object",
    "examples": [
      {
        "model": "gemini-2.5-flash",
        "api_key": "<your-api-key>",
        "image_url": "https://www.ia-qa.com/media/discover-report-poster.png",
        "test_cases": [
          {
            "question": "Does the image contain text? Answer yes or no.",
            "assertion_type": "contains",
            "assertion_value": "yes"
          }
        ]
      }
    ],
    "required": [
      "test_cases",
      "model",
      "api_key"
    ],
    "properties": {
      "model": {
        "enum": [
          "gpt-4o",
          "gpt-4o-mini",
          "claude-sonnet-4-6",
          "claude-haiku-4-5-20251001",
          "gemini-3.5-flash",
          "gemini-2.5-flash"
        ],
        "type": "string"
      },
      "api_key": {
        "type": "string",
        "description": "Key for the model's provider."
      },
      "image_url": {
        "type": "string",
        "description": "Public image URL (or image_base64)."
      },
      "threshold": {
        "type": "number",
        "description": "Pass rate for an overall PASS (default 80)."
      },
      "test_cases": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "question"
          ],
          "properties": {
            "id": {
              "type": "string"
            },
            "question": {
              "type": "string"
            },
            "assertion_type": {
              "enum": [
                "contains",
                "not_contains",
                "json_format",
                "min_length",
                "max_length",
                "semantic_contains"
              ],
              "type": "string"
            },
            "assertion_value": {
              "type": "string",
              "description": "Not needed for json_format."
            }
          }
        },
        "maxItems": 10
      },
      "image_base64": {
        "type": "string"
      },
      "system_prompt": {
        "type": "string"
      },
      "image_mime_type": {
        "type": "string",
        "description": "For image_base64 (default image/jpeg)."
      }
    }
  }

  arguments 90 lines copy
• run_vlm_test_suite_batch unknown never probed

  Compare several vision models on the same image and test cases, in parallel (same assertions as run_vlm_test_suite). Returns `modelResults`: per model, the run_vlm_test_suite result (or its error). BYOK: one key per model.

  mcp-tool

  123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102

  {
    "type": "object",
    "examples": [
      {
        "models": [
          "gpt-4o-mini",
          "gemini-2.5-flash"
        ],
        "api_keys": {
          "gpt-4o-mini": "<your-openai-key>",
          "gemini-2.5-flash": "<your-google-key>"
        },
        "image_url": "https://www.ia-qa.com/media/discover-report-poster.png",
        "test_cases": [
          {
            "question": "Does the image contain text? Answer yes or no."
          }
        ]
      }
    ],
    "required": [
      "test_cases",
      "models",
      "api_keys"
    ],
    "properties": {
      "models": {
        "type": "array",
        "items": {
          "enum": [
            "gpt-4o",
            "gpt-4o-mini",
            "claude-sonnet-4-6",
            "claude-haiku-4-5-20251001",
            "gemini-3.5-flash",
            "gemini-2.5-flash"
          ],
          "type": "string"
        },
        "maxItems": 6,
        "minItems": 1
      },
      "api_keys": {
        "type": "object",
        "description": "Model id → API key.",
        "additionalProperties": {
          "type": "string"
        }
      },
      "image_url": {
        "type": "string",
        "description": "Public image URL (or image_base64)."
      },
      "threshold": {
        "type": "number",
        "description": "Pass rate for an overall PASS (default 80)."
      },
      "test_cases": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "question"
          ],
          "properties": {
            "id": {
              "type": "string"
            },
            "question": {
              "type": "string"
            },
            "assertion_type": {
              "enum": [
                "contains",
                "not_contains",
                "json_format",
                "min_length",
                "max_length",
                "semantic_contains"
              ],
              "type": "string"
            },
            "assertion_value": {
              "type": "string",
              "description": "Not needed for json_format."
            }
          }
        },
        "maxItems": 10
      },
      "image_base64": {
        "type": "string"
      },
      "system_prompt": {
        "type": "string"
      },
      "image_mime_type": {
        "type": "string",
        "description": "For image_base64 (default image/jpeg)."
      }
    }
  }

  arguments 102 lines copy
• rerank_evaluate unknown 6d ago

  Evaluate RAG retrieval: rank passages against a query, compute Precision@k / Recall@k and a PASS/FAIL verdict from your relevance labels (INCONCLUSIVE without them). Modes: (1) give every passage the `score` YOUR reranker produced — deterministic, offline, the mode to gate CI on; (2) no scores, no key — a local BM25 keyword baseline, never a judgement of a neural reranker; (3) api_key for an NVIDIA account that still has reranking (NVIDIA retired the hosted endpoints on 2026-05-18).

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970

  {
    "type": "object",
    "examples": [
      {
        "query": "capital of France",
        "api_key": "<your-api-key>",
        "passages": [
          {
            "text": "Paris is the capital of France.",
            "relevant": true
          },
          {
            "text": "Bananas are rich in potassium.",
            "relevant": false
          }
        ]
      }
    ],
    "required": [
      "query",
      "passages"
    ],
    "properties": {
      "query": {
        "type": "string"
      },
      "top_k": {
        "type": "integer",
        "maximum": 10,
        "minimum": 1,
        "description": "Default 3."
      },
      "api_key": {
        "type": "string",
        "description": "NVIDIA key, used only when no passage has a score; never stored."
      },
      "passages": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "text"
          ],
          "properties": {
            "id": {
              "type": "string"
            },
            "text": {
              "type": "string"
            },
            "score": {
              "type": "number",
              "description": "Your reranker's score (higher = more relevant). On all passages or none."
            },
            "relevant": {
              "type": "boolean",
              "description": "Ground truth."
            }
          }
        },
        "description": "2 to 20 passages."
      },
      "threshold": {
        "type": "number",
        "maximum": 1,
        "minimum": 0,
        "description": "Minimum Precision@k to PASS (default 0.5)."
      }
    }
  }

  arguments 70 lines copy
• security_headers_check reads unknown 23h ago

  Analyse the HTTP security headers of a public URL OR of raw response headers you paste in. Grades each header (A–F) for: Strict-Transport-Security, Content-Security-Policy, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, Permissions-Policy, X-XSS-Protection, Cross-Origin-Opener-Policy, Cross-Origin-Resource-Policy, and Cross-Origin-Embedder-Policy. Returns an overall score (0–100), per-header grades, missing headers, and fix snippets for Express, Nginx, and Apache. For localhost/private targets the remote server cannot reach, pass the `headers` parameter instead of `url`.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "url": "https://www.ia-qa.com"
      }
    ],
    "properties": {
      "url": {
        "type": "string",
        "description": "Optional. Full public URL to check (e.g. https://example.com). Omit it entirely when using `headers`. The server cannot reach localhost/private IPs."
      },
      "headers": {
        "description": "Optional, and sufficient on its own (no url needed). The response headers to grade, either as an object {\"strict-transport-security\": \"max-age=...\", ...} or as the raw header block pasted as a string (e.g. `curl -sI` output). Use this to audit a local server the remote MCP cannot reach."
      }
    }
  }

  arguments 17 lines copy
• llm_format_check unknown 16h ago

  Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for structured output testing.

  mcp-tool

  1234567891011121314151617181920212223242526272829303132333435363738

  {
    "type": "object",
    "examples": [
      {
        "output": "a: 1\nb: [1, 2]",
        "expected_format": "yaml"
      }
    ],
    "required": [
      "output",
      "expected_format"
    ],
    "properties": {
      "output": {
        "type": "string",
        "description": "The LLM output to validate"
      },
      "regex_pattern": {
        "type": "string",
        "description": "Custom regex pattern (only when expected_format is \"regex\")"
      },
      "expected_format": {
        "enum": [
          "json",
          "markdown_heading",
          "code_block",
          "bullet_list",
          "numbered_list",
          "table",
          "yaml",
          "xml",
          "regex"
        ],
        "type": "string",
        "description": "Expected format"
      }
    }
  }

  arguments 38 lines copy
• vector_similarity reads unknown 3d ago

  Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scoring, embedding evaluation, and nearest-neighbor testing.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546

  {
    "type": "object",
    "examples": [
      {
        "vector_a": [
          1,
          0
        ],
        "vector_b": [
          0,
          1
        ]
      }
    ],
    "required": [
      "vector_a",
      "vector_b"
    ],
    "properties": {
      "metric": {
        "enum": [
          "cosine",
          "dot_product",
          "euclidean",
          "manhattan",
          "all"
        ],
        "type": "string",
        "description": "Distance metric (default: all)"
      },
      "vector_a": {
        "type": "array",
        "items": {
          "type": "number"
        },
        "description": "First vector as array of floats"
      },
      "vector_b": {
        "type": "array",
        "items": {
          "type": "number"
        },
        "description": "Second vector as array of floats"
      }
    }
  }

  arguments 46 lines copy
• normalize_vector unknown 2d ago

  L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 1000 vectors.

  mcp-tool

  123456789101112131415161718192021222324252627282930

  {
    "type": "object",
    "examples": [
      {
        "vector": [
          3,
          4
        ]
      }
    ],
    "properties": {
      "batch": {
        "type": "array",
        "items": {
          "type": "array",
          "items": {
            "type": "number"
          }
        },
        "description": "Batch of vectors to normalize (overrides vector)"
      },
      "vector": {
        "type": "array",
        "items": {
          "type": "number"
        },
        "description": "Single vector to normalize"
      }
    }
  }

  arguments 30 lines copy
• validate_url unknown 4d ago

  Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "https://www.ia-qa.com/mcp?x=1#y"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "URL to validate and parse"
      }
    }
  }

  arguments 17 lines copy
• parse_csv unknown 6d ago

  Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets multi-line cell), and doubled quotes. Custom delimiters supported. An unterminated quote is rejected with its position rather than parsed into corrupted rows. Use when processing spreadsheet exports, data imports, or structured text pipelines where the source is CSV. Supports up to 200 KB.

  mcp-tool

  1234567891011121314151617181920212223242526

  {
    "type": "object",
    "examples": [
      {
        "input": "a;b\n1;2",
        "delimiter": ";"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "CSV content to parse"
      },
      "header": {
        "type": "boolean",
        "description": "Treat the first row as headers (default: true)"
      },
      "delimiter": {
        "type": "string",
        "description": "Field delimiter character (default: \",\")"
      }
    }
  }

  arguments 26 lines copy
• merge_json unknown 9h ago

  Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — override takes precedence for primitives.

  mcp-tool

  12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849

  {
    "type": "object",
    "examples": [
      {
        "base": {
          "a": {
            "x": 1,
            "y": 2
          }
        },
        "override": {
          "a": {
            "y": 3
          }
        }
      }
    ],
    "required": [
      "base",
      "override"
    ],
    "properties": {
      "base": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The base JSON object (merged into) — a JSON string, or the object itself."
      },
      "override": {
        "type": [
          "string",
          "object",
          "array"
        ],
        "description": "The override JSON object (takes precedence) — a JSON string, or the object itself."
      },
      "array_strategy": {
        "enum": [
          "replace",
          "concat",
          "unique"
        ],
        "type": "string",
        "description": "Array merge strategy: replace (default), concat, or unique"
      }
    }
  }

  arguments 49 lines copy
• color_convert reads unknown 3d ago

  Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color values from user input. Accepts #rrggbb, #rgb, rgb(r,g,b), or hsl(h,s%,l%).

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "rgb(18, 52, 86)"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Color value to convert, e.g. \"#ff6b6b\", \"rgb(255,107,107)\", \"hsl(0,100%,71%)\""
      }
    }
  }

  arguments 17 lines copy
• detect_secrets unknown never probed

  Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, Slack/Teams/Discord webhook URLs, session cookies, database connection strings, and generic passwords. Returns findings with severity.

  mcp-tool

  123456789101112131415161718192021

  {
    "type": "object",
    "examples": [
      {
        "input": "redis://cache:6379"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Code or config content to scan (max 500KB)"
      },
      "filename": {
        "type": "string",
        "description": "Optional filename for context (e.g. \".env\", \"config.js\")"
      }
    }
  }

  arguments 21 lines copy
• base64_decode reads unknown 3d ago

  Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "aMOpbGxvIPCfjI0="
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "Base64 string to decode"
      }
    }
  }

  arguments 17 lines copy
• unescape_html reads unknown 4d ago

  Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email content, or legacy database fields before passing to an LLM or displaying to users.

  mcp-tool

  1234567891011121314151617

  {
    "type": "object",
    "examples": [
      {
        "input": "😀 😀"
      }
    ],
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "string",
        "description": "HTML-encoded string to unescape"
      }
    }
  }

  arguments 17 lines copy