modellwerk

endpoint

https://modell.halowerk.com/mcp

door code

8f9aec848cabdc7d

protocol

streamable-http ·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 modellwerk live?

Yes — it answered the hub's last check (checked 2h ago). It answered 100% of checks over the last 30 days.

Is modellwerk free to use?

No — it charges per call, from 0.002 USDC, quoted in its own 402 answers.

What tools does modellwerk have?

15 tools: openapi_validate, drift_check, trace_grade, llm_catalog, model_router, context_plan, output_validate, tool_arg_validate, ….

Is modellwerk safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

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.

• openapi_validate reads 0.002 USDC paid 5h ago

  Reads an OpenAPI 3.x document and reports what would stop a machine consumer. Errors: missing openapi, info or paths; a path that declares no method; an internal $ref that resolves to nothing; a parameter without a name, location or schema; a request body without content. Warnings: an operation without operationId (client generators fall back to guessing), a response without a schema, a 2xx response missing entirely, no example anywhere on an operation, a path parameter that appears in the URL but is never declared. External $refs to other files are reported as unresolvable here rather than treated as errors, since only the document handed in is read. Swagger 2.0 is detected and rejected with a note rather than validated against the wrong rules. Preis 0.002 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "document"
    ],
    "properties": {
      "document": {
        "type": "object",
        "description": "The OpenAPI document as JSON."
      },
      "require_examples": {
        "type": "boolean",
        "default": true,
        "description": "Report operations with no example as a warning."
      },
      "require_operation_ids": {
        "type": "boolean",
        "default": true,
        "description": "Report operations without operationId as a warning."
      }
    },
    "additionalProperties": false
  }

  arguments 23 lines copy
• drift_check reads 0.003 USDC paid 18h ago

  Compares two submitted sample sets feature by feature and returns deterministic drift metrics. Continuous features are binned on the combined value range and receive PSI, Jensen-Shannon divergence and a Kolmogorov-Smirnov statistic; categorical features receive PSI and Jensen-Shannon divergence over exact category labels. The response names every feature, its sample counts, the metric values, the threshold used, and the reason a feature was flagged. It is a distribution check over your provided data only: it does not fetch production telemetry, store samples, retrain a model or claim business impact. Preis 0.003 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "reference_samples",
      "current_samples"
    ],
    "properties": {
      "bins": {
        "type": "integer",
        "default": 10,
        "maximum": 50,
        "minimum": 2,
        "description": "Number of equal-width bins for continuous features."
      },
      "threshold": {
        "type": "number",
        "default": 0.2,
        "maximum": 1,
        "minimum": 0.01,
        "description": "Drift threshold applied to PSI for the final feature flag."
      },
      "feature_types": {
        "type": "object",
        "description": "Optional explicit feature types keyed by feature name.",
        "maxProperties": 50,
        "additionalProperties": {
          "enum": [
            "continuous",
            "categorical"
          ],
          "type": "string"
        }
      },
      "current_samples": {
        "type": "object",
        "description": "Current samples keyed by the same feature names as reference_samples.",
        "maxProperties": 50,
        "minProperties": 1,
        "additionalProperties": {
          "type": "array",
          "items": {
            "type": [
              "number",
              "string",
              "boolean"
            ]
          },
          "maxItems": 10000,
          "minItems": 2
        }
      },
      "reference_samples": {
        "type": "object",
        "description": "Baseline samples keyed by feature name. Each value is an array of finite numbers for continuous features or scalar category labels for categorical features.",
        "maxProperties": 50,
        "minProperties": 1,
        "additionalProperties": {
          "type": "array",
          "items": {
            "type": [
              "number",
              "string",
              "boolean"
            ]
          },
          "maxItems": 10000,
          "minItems": 2
        }
      }
    },
    "additionalProperties": false
  }

  arguments 72 lines copy
• trace_grade reads 0.002 USDC paid 20h ago

  Takes the steps of one agent run and returns a scored report. It counts identical tool calls (same tool, same arguments) as loops, finds the longest run of consecutive failures, marks calls whose result was empty or null as wasted, and attributes cost per step and per tool so the expensive part is visible. Cost is taken from your figures when supplied, otherwise computed from token counts against the model price table. A grade from A to F is derived from four measurable ratios — loops, errors, empty results and cost concentration — and every ratio is returned with the threshold it was judged against, so the grade can be recomputed or disagreed with. It judges shape and spend only: a run can be efficient and still produce the wrong answer, and nothing here checks the answer. Preis 0.002 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "steps"
    ],
    "properties": {
      "model": {
        "type": "string",
        "description": "Model id used, to price token counts when cost_usd is absent."
      },
      "steps": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "ok": {
              "type": "boolean",
              "description": "Whether the step succeeded. Defaults to true unless error is set."
            },
            "tool": {
              "type": "string",
              "description": "Name of the tool or step."
            },
            "error": {
              "type": "string",
              "description": "Error text, if it failed."
            },
            "result": {
              "description": "The result. Empty, null or an empty list counts as a wasted call."
            },
            "cost_usd": {
              "type": "number",
              "minimum": 0,
              "description": "Cost of this step, if you already know it."
            },
            "arguments": {
              "description": "The arguments used. Identical arguments on the same tool count as a loop."
            },
            "duration_ms": {
              "type": "integer",
              "minimum": 0
            },
            "input_tokens": {
              "type": "integer",
              "minimum": 0
            },
            "output_tokens": {
              "type": "integer",
              "minimum": 0
            }
          },
          "additionalProperties": true
        },
        "maxItems": 1000,
        "minItems": 1,
        "description": "The steps of the run, in order."
      },
      "loop_threshold": {
        "type": "integer",
        "default": 2,
        "minimum": 2,
        "description": "From how many identical calls on it counts as a loop."
      }
    },
    "additionalProperties": false
  }

  arguments 66 lines copy
• llm_catalog reads 0.005 USDC paid 15h ago

  A comparison table of language models: input, output and cache prices per million tokens, context window, maximum output length, batch discount and a short note on what each is suited to. Filter by vendor, by a maximum price, by the context window you need, or by capability tag. Every row carries the date its prices were last checked, so a stale figure is visible rather than silently wrong. This is a curated table, not a live vendor query: prices are the publicly published list rates and negotiated or promotional rates are not represented. Availability, rate limits per account and regional restrictions are not covered — they depend on your contract, not on the model. Preis 0.005 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "tag": {
        "type": "string",
        "maxLength": 40,
        "description": "Only models carrying this capability tag, e.g. coding, speed, reasoning, cheap."
      },
      "sort": {
        "enum": [
          "input_price",
          "output_price",
          "context",
          "vendor"
        ],
        "type": "string",
        "default": "output_price",
        "description": "Sort order."
      },
      "vendor": {
        "type": "string",
        "maxLength": 64,
        "description": "Only models from this vendor, e.g. anthropic, openai, google."
      },
      "min_output": {
        "type": "integer",
        "minimum": 0,
        "description": "Only models able to emit at least this many output tokens."
      },
      "min_context": {
        "type": "integer",
        "minimum": 0,
        "description": "Only models whose context window is at least this many tokens."
      },
      "max_input_price": {
        "type": "number",
        "minimum": 0,
        "description": "Only models at or below this input price per million tokens."
      },
      "max_output_price": {
        "type": "number",
        "minimum": 0,
        "description": "Only models at or below this output price per million tokens."
      }
    },
    "additionalProperties": false
  }

  arguments 47 lines copy
• model_router reads 0.002 USDC paid 15h ago

  Scores every catalogue model against one task and returns a ranked shortlist. You state the task kind, optionally a per-call budget in USD, how much speed matters, the input size and any vendor restriction; the answer names a model, the estimated cost per call, and the sub-scores it was chosen on, so the ranking can be checked rather than trusted. Models that cannot hold the input are excluded and listed separately with the reason. Weights are yours to set and are echoed back. The routing table is a heuristic over published prices and capability tags, not a benchmark: it will not tell you which model is more accurate on your data, and for anything quality-critical it is a shortlist to evaluate, not a verdict. Preis 0.002 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "task"
    ],
    "properties": {
      "task": {
        "enum": [
          "classification",
          "extraction",
          "summarization",
          "translation",
          "chat",
          "coding",
          "agent",
          "reasoning",
          "research",
          "vision",
          "long_context"
        ],
        "type": "string",
        "description": "What the model has to do."
      },
      "weights": {
        "type": "object",
        "properties": {
          "cost": {
            "type": "number",
            "default": 0.4,
            "minimum": 0
          },
          "speed": {
            "type": "number",
            "default": 0.2,
            "minimum": 0
          },
          "capability": {
            "type": "number",
            "default": 0.4,
            "minimum": 0
          }
        },
        "description": "Weighting of cost, capability and speed. Normalised to sum 1.",
        "additionalProperties": false
      },
      "input_text": {
        "type": "string",
        "maxLength": 100000,
        "description": "Sample input. Used to estimate input_tokens if that is not given."
      },
      "input_tokens": {
        "type": "integer",
        "default": 2000,
        "minimum": 0,
        "description": "Expected input size in tokens."
      },
      "require_tags": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Capabilities the model must carry, e.g. vision."
      },
      "output_tokens": {
        "type": "integer",
        "minimum": 0,
        "description": "Expected output length. Defaults to a value typical for the task."
      },
      "speed_priority": {
        "enum": [
          "low",
          "normal",
          "high"
        ],
        "type": "string",
        "default": "normal",
        "description": "How much latency matters."
      },
      "allowed_vendors": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Restrict to these vendors, e.g. for a data-residency or procurement rule."
      },
      "excluded_vendors": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Exclude these vendors."
      },
      "budget_usd_per_call": {
        "type": "number",
        "minimum": 0,
        "description": "Ceiling per call. Models above it are excluded."
      }
    },
    "additionalProperties": false
  }

  arguments 100 lines copy
• context_plan reads 0.002 USDC paid 18h ago

  Plans the division of a long input for a given model. It reserves room for the system prompt, the expected answer and a safety margin, then works out how many chunks are needed and where to cut, preferring paragraph boundaries over line and sentence boundaries and never cutting inside a word. Optional overlap carries context across the seams. The result gives per-chunk token estimates, the character offsets of every cut, the boundary type actually used, and the total cost for processing all chunks on that model. If the text fits in one pass, it says so and stops. Token counts are estimates from character and word statistics, not a tokeniser: leave the default margin in place, or verify with the vendor counter where an overrun would be expensive. Preis 0.002 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "text",
      "model"
    ],
    "properties": {
      "text": {
        "type": "string",
        "maxLength": 2000000,
        "minLength": 1,
        "description": "The document to split."
      },
      "model": {
        "type": "string",
        "description": "Target model id, e.g. claude-opus-5."
      },
      "boundary": {
        "enum": [
          "absatz",
          "zeile",
          "satz",
          "auto"
        ],
        "type": "string",
        "default": "auto",
        "description": "Where to cut. auto tries paragraph, then line, then sentence."
      },
      "safety_margin": {
        "type": "number",
        "default": 0.1,
        "maximum": 0.5,
        "minimum": 0,
        "description": "Fraction of the window held back against estimation error. 0.1 = 10 percent."
      },
      "overlap_tokens": {
        "type": "integer",
        "default": 0,
        "maximum": 20000,
        "minimum": 0,
        "description": "Tokens of the previous chunk repeated at the start of the next."
      },
      "system_prompt_tokens": {
        "type": "integer",
        "default": 500,
        "minimum": 0,
        "description": "Room to reserve for the system prompt per chunk."
      },
      "expected_output_tokens": {
        "type": "integer",
        "default": 2000,
        "minimum": 0,
        "description": "Room to reserve for the answer per chunk."
      }
    },
    "additionalProperties": false
  }

  arguments 57 lines copy
• output_validate reads 0.002 USDC paid never probed

  Validates output against a JSON Schema and reports each violation with its JSON path, the rule it broke and a sentence saying what to change. Accepts either a parsed object or the raw string a model returned: a payload wrapped in a code fence or surrounded by prose is unwrapped, and the wrapping is reported separately from schema errors so you fix the right layer. Draft 2020-12 and draft-07 are both supported, along with the common string formats. Optionally the repairable problems are listed: missing fields that have a default, and unexpected extra fields. It validates structure, never truth — a document that satisfies the schema perfectly can still be factually wrong, and nothing here checks that. Preis 0.002 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "schema"
    ],
    "properties": {
      "output": {
        "description": "The model answer, as a parsed JSON value. Either this or output_text."
      },
      "schema": {
        "type": "object",
        "description": "JSON Schema to validate against."
      },
      "all_errors": {
        "type": "boolean",
        "default": true,
        "description": "Report every violation instead of stopping at the first."
      },
      "output_text": {
        "type": "string",
        "maxLength": 400000,
        "description": "The raw answer as text. Code fences and surrounding prose are stripped."
      },
      "suggest_repairs": {
        "type": "boolean",
        "default": true,
        "description": "List repairable problems (defaultable missing fields, extra fields)."
      }
    },
    "additionalProperties": false
  }

  arguments 31 lines copy
• tool_arg_validate reads 0.002 USDC paid 5h ago

  Two checks in one. First the arguments are validated against the tool input schema and every violation is reported with its path and a sentence saying what to change. Second, values that satisfy the schema but look wrong are flagged: an unset required field filled with a placeholder like "string" or "TODO", a number at a suspicious order of magnitude, an address that fails its checksum shape, a destructive flag set to true, an empty string where content was expected. Fields whose names suggest an irreversible effect — amounts, recipients, deletion and force flags — are held to the stricter standard. Warnings are heuristics and can be wrong in both directions: treat them as a prompt to look, not a verdict, and never as a substitute for the tool own checks. Preis 0.002 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "arguments",
      "schema"
    ],
    "properties": {
      "schema": {
        "type": "object",
        "description": "The tool input schema."
      },
      "arguments": {
        "type": "object",
        "description": "The arguments about to be sent."
      },
      "tool_name": {
        "type": "string",
        "maxLength": 128,
        "description": "Name of the tool, carried into the report."
      },
      "irreversible": {
        "type": "boolean",
        "default": false,
        "description": "Mark the call as irreversible. Raises every warning one level."
      },
      "expected_magnitude": {
        "type": "object",
        "description": "Optional per-field order-of-magnitude hint, e.g. {\"amount\": 100}. A value more than 100x off is flagged.",
        "additionalProperties": {
          "type": "number"
        }
      }
    },
    "additionalProperties": false
  }

  arguments 35 lines copy
• llm_catalog_diff reads 0.005 USDC paid 20h ago

  Use this when an agent has an older and a newer LLM catalog snapshot and needs a deterministic migration diff before changing routing or budgets. Rows are matched by vendor plus model, id or name. The result separates added, removed and changed models and reports field-level differences for input, output and cached-input price, context window and maximum output tokens. Both snapshots come from the caller: this endpoint does not fetch vendor data, infer missing values, judge whether a change is beneficial or store either catalog. Preis 0.005 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "examples": [
      {
        "after": [
          {
            "model": "model-small",
            "vendor": "example-ai",
            "context_window": 64000,
            "input_price_per_million": 0.8,
            "output_price_per_million": 3
          }
        ],
        "before": [
          {
            "model": "model-small",
            "vendor": "example-ai",
            "context_window": 32000,
            "input_price_per_million": 1,
            "output_price_per_million": 3
          }
        ]
      }
    ],
    "required": [
      "before",
      "after"
    ],
    "properties": {
      "after": {
        "type": "array",
        "items": {
          "type": "object",
          "anyOf": [
            {
              "required": [
                "model"
              ]
            },
            {
              "required": [
                "id"
              ]
            },
            {
              "required": [
                "name"
              ]
            }
          ],
          "properties": {
            "id": {
              "type": "string",
              "maxLength": 160,
              "minLength": 1
            },
            "name": {
              "type": "string",
              "maxLength": 160,
              "minLength": 1
            },
            "model": {
              "type": "string",
              "maxLength": 160,
              "minLength": 1
            },
            "vendor": {
              "type": "string",
              "maxLength": 64,
              "description": "Provider name. Empty is allowed when model identifiers are globally unique."
            },
            "context_window": {
              "type": [
                "integer",
                "null"
              ],
              "minimum": 0
            },
            "max_output_tokens": {
              "type": [
                "integer",
                "null"
              ],
              "minimum": 0
            },
            "input_price_per_million": {
              "type": [
                "number",
                "null"
              ],
              "minimum": 0
            },
            "output_price_per_million": {
              "type": [
                "number",
                "null"
              ],
              "minimum": 0
            },
            "cached_input_price_per_million": {
              "type": [
                "number",
                "null"
              ],
              "minimum": 0
            }
          },
          "additionalProperties": true
        },
        "maxItems": 500,
        "minItems": 1,
        "description": "Newer catalog snapshot."
      },
      "before": {
        "type": "array",
        "items": {
          "type": "object",
          "anyOf": [
            {
              "required": [
                "model"
              ]
            },
            {
              "required": [
                "id"
              ]
            },
            {
              "required": [
                "name"
              ]
            }
          ],
          "properties": {
            "id": {
              "type": "string",
              "maxLength": 160,
              "minLength": 1
            },
            "name": {
              "type": "string",
              "maxLength": 160,
              "minLength": 1
            },
            "model": {
              "type": "string",
              "maxLength": 160,
              "minLength": 1
            },
            "vendor": {
              "type": "string",
              "maxLength": 64,
              "description": "Provider name. Empty is allowed when model identifiers are globally unique."
            },
            "context_window": {
              "type": [
                "integer",
                "null"
              ],
              "minimum": 0
            },
            "max_output_tokens": {
              "type": [
                "integer",
                "null"
              ],
              "minimum": 0
            },
            "input_price_per_million": {
              "type": [
                "number",
                "null"
              ],
              "minimum": 0
            },
            "output_price_per_million": {
              "type": [
                "number",
                "null"
              ],
              "minimum": 0
            },
            "cached_input_price_per_million": {
              "type": [
                "number",
                "null"
              ],
              "minimum": 0
            }
          },
          "additionalProperties": true
        },
        "maxItems": 500,
        "minItems": 1,
        "description": "Older catalog snapshot."
      }
    },
    "additionalProperties": false
  }

  arguments 200 lines copy
• text_embeddings 0.003 USDC paid 8h ago

  Runs nomic-embed-text locally through Ollama and returns 768-dimensional embeddings. No third-party text transfer. Input is rejected rather than silently truncated; at most four 1000-character strings per request. Preis 0.003 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "input"
    ],
    "properties": {
      "input": {
        "type": "array",
        "items": {
          "type": "string",
          "maxLength": 1000,
          "minLength": 1
        },
        "maxItems": 4,
        "minItems": 1
      }
    },
    "additionalProperties": false
  }

  arguments 19 lines copy
• document_rerank 0.003 USDC paid 8h ago

  Embeds a query and at most 20 short documents on local nomic-embed-text, then sorts by cosine similarity with stable input-index tie breaks. Scores are not probabilities. No third-party text transfer; input is not silently truncated. Preis 0.003 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "query",
      "documents"
    ],
    "properties": {
      "query": {
        "type": "string",
        "maxLength": 1000,
        "minLength": 1
      },
      "top_n": {
        "type": "integer",
        "maximum": 20,
        "minimum": 1
      },
      "documents": {
        "type": "array",
        "items": {
          "type": "string",
          "maxLength": 1000,
          "minLength": 1
        },
        "maxItems": 20,
        "minItems": 1
      },
      "return_documents": {
        "type": "boolean",
        "default": false
      }
    },
    "additionalProperties": false
  }

  arguments 34 lines copy
• cost_estimate reads 0.002 USDC paid 15h ago

  Works out what a call would cost. Give either the text itself or a token count, plus the expected output length, and get input, output, cache-read and cache-write cost per model with the total. Token counts derived from text are estimates from character and word statistics, not a tokeniser, and typically land within about 15 percent — where an exact count matters, count with the vendor tokeniser. Prices come from a table with a stated date; a model whose price has moved since is reported with its table date, so an old figure is visible rather than silently wrong. Unknown model names are refused with the closest matches rather than guessed. Preis 0.002 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "text": {
        "type": "string",
        "maxLength": 200000,
        "description": "The prompt text. Either this or input_tokens."
      },
      "batch": {
        "type": "boolean",
        "default": false,
        "description": "Apply the batch discount where the model has one."
      },
      "calls": {
        "type": "integer",
        "default": 1,
        "maximum": 1000000,
        "minimum": 1,
        "description": "Number of identical calls to multiply by."
      },
      "models": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "maxItems": 20,
        "description": "Model ids to price. All known models if omitted."
      },
      "input_tokens": {
        "type": "integer",
        "maximum": 10000000,
        "minimum": 0,
        "description": "Known input token count. Either this or text."
      },
      "output_tokens": {
        "type": "integer",
        "default": 500,
        "maximum": 1000000,
        "minimum": 0,
        "description": "Expected output length in tokens."
      },
      "cached_input_tokens": {
        "type": "integer",
        "minimum": 0,
        "description": "Of the input tokens, how many are served from cache."
      }
    },
    "additionalProperties": false
  }

  arguments 49 lines copy
• tool_compat reads 0.002 USDC paid 20h ago

  Compares a producer output schema against a consumer input schema and reports whether the two can be chained. Every required field of the consumer is looked for in the producer: exact name matches first, then well-known synonyms (url/uri/link, id/key/ref, created_at/timestamp and so on), which are reported as renames needing a mapping rather than as clean matches. Types are checked for direction: integer into number is widening, number into integer loses precision, anything into string is a conversion, string into anything is a parse that can fail. Enum values are intersected, so a producer that can emit a value the consumer does not accept is flagged. The result is a field mapping you can implement directly, plus the list of gaps no mapping can close. Preis 0.002 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "producer_schema",
      "consumer_schema"
    ],
    "properties": {
      "use_synonyms": {
        "type": "boolean",
        "default": true,
        "description": "Accept well-known renames as a match."
      },
      "consumer_name": {
        "type": "string",
        "maxLength": 128,
        "description": "Name of the consuming tool, for the report."
      },
      "producer_name": {
        "type": "string",
        "maxLength": 128,
        "description": "Name of the producing tool, for the report."
      },
      "consumer_schema": {
        "type": "object",
        "description": "Input schema of the second tool."
      },
      "producer_schema": {
        "type": "object",
        "description": "Output schema of the first tool."
      }
    },
    "additionalProperties": false
  }

  arguments 33 lines copy
• openapi_to_mcp reads 0.002 USDC paid 5h ago

  Converts each OpenAPI operation into an MCP tool. Path, query and header parameters and the JSON request body are merged into one input schema, because MCP tools take exactly one; a name collision between a body field and a parameter is reported rather than silently overwritten. Internal $refs are expanded inline so each tool schema stands alone — an MCP client cannot resolve them. Tool names come from operationId, normalised to snake_case, or are derived from method and path when it is missing. Read-only operations are marked with readOnlyHint and write operations with destructiveHint. Only application/json bodies are converted: multipart, form and binary bodies are listed as skipped with the reason. The result is a starting point, not a finished server — it carries no auth handling and no request execution. Preis 0.002 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "document"
    ],
    "properties": {
      "naming": {
        "enum": [
          "snake_case",
          "preserve"
        ],
        "type": "string",
        "default": "snake_case",
        "description": "How to normalise tool names."
      },
      "prefix": {
        "type": "string",
        "maxLength": 40,
        "description": "Prefix for every tool name, to keep several APIs apart in one client."
      },
      "document": {
        "type": "object",
        "description": "The OpenAPI 3.x document as JSON."
      },
      "include_methods": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "description": "Only convert these HTTP methods, e.g. [\"get\"] for a read-only server."
      }
    },
    "additionalProperties": false
  }

  arguments 34 lines copy
• chat_completions 0.003 USDC paid 8h ago

  Runs openai/gpt-oss-20b via GroqCloud. Messages are sent to Groq Inc., USA; payment metadata is not forwarded. At most 8 messages, 8192 UTF-8 bytes overall and 1024 completion tokens including reasoning. A length finish_reason exposes truncation. No tool execution, browsing or API key resale. Preis 0.003 USDC je Aufruf, Abrechnung über x402.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "messages"
    ],
    "properties": {
      "messages": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "role",
            "content"
          ],
          "properties": {
            "role": {
              "enum": [
                "system",
                "user",
                "assistant"
              ]
            },
            "content": {
              "type": "string",
              "maxLength": 4000,
              "minLength": 1
            }
          },
          "additionalProperties": false
        },
        "maxItems": 8,
        "minItems": 1
      }
    },
    "additionalProperties": false
  }

  arguments 36 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.

Served from the same domain, which is what was measured. Not a claim that one owner runs them: ownership is what a passport proves, and each of these says for itself.

• datenvertrag.halowerk.com datenvertragswerk mcp · 340 tools
• medien.halowerk.com medienwerk mcp · 317 tools
• lauf.halowerk.com laufwerk mcp · 271 tools
• finops.halowerk.com finopswerk mcp · 261 tools
• buero.halowerk.com buerowerk mcp · 248 tools
• seo.halowerk.com seowerk mcp · 170 tools
• commerce-central.halowerk.com commerce-central mcp · 150 tools
• commerce-south.halowerk.com commerce-south mcp · 150 tools

101 more sit on this domain. All of them.