ediscovery-decoder-news-calc

endpoint

https://mcp.ediscoverydecoder.com/api/mcp

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 ediscovery-decoder-news-calc live?

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

Is ediscovery-decoder-news-calc free to use?

Yes — the hub reached it with no key and no payment.

What tools does ediscovery-decoder-news-calc have?

15 tools: calculate_elusion, calculate_prevalence_richness, calculate_review_metrics, calculate_tar_recall_estimate, calculate_control_set_recall, validate_sample_design, calculate_sample_size, get_prompt_template, ….

Is ediscovery-decoder-news-calc 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.

• get_news_brief reads open 11h ago

  Get the current eDiscovery Decoder news brief: top stories plus a Week in Review breakdown, returned both as structured data and as display-ready Markdown (formatted_brief) with a 'why it matters' line per story. Use when the user wants a roundup or summary of current eDiscovery AI news rather than a keyword search.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "properties": {
      "week_limit": {
        "type": "integer",
        "default": 3,
        "maximum": 10,
        "minimum": 1
      },
      "current_limit": {
        "type": "integer",
        "default": 7,
        "maximum": 20,
        "minimum": 1
      }
    },
    "additionalProperties": false
  }

  arguments 19 lines copy
• search_news reads open 11h ago

  Find recent eDiscovery / legal-AI / TAR news by topic, tag, or date range. Use when the user asks what's new or recent in eDiscovery, wants stories on a subject, or asks about a time window. For a ready-made top-stories roundup instead, use get_news_brief.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "properties": {
      "tags": {
        "type": "array",
        "items": {
          "type": "string",
          "minLength": 1
        }
      },
      "limit": {
        "type": "integer",
        "default": 10,
        "maximum": 50,
        "minimum": 1
      },
      "query": {
        "type": "string",
        "minLength": 1
      },
      "date_to": {
        "$ref": "#/properties/date_from"
      },
      "date_from": {
        "type": "string",
        "pattern": "^\\d{4}-\\d{2}-\\d{2}$"
      }
    },
    "additionalProperties": false
  }

  arguments 31 lines copy
• ping reads open 15h ago

  Health check: confirm the eDiscovery Decoder News/Calc MCP server is reachable before a demo or when troubleshooting a connection. Returns server name and version. No inputs.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "properties": {}
  }

  arguments 5 lines copy
• get_demo_guide reads open 11h ago

  Return a short, human-readable walkthrough for testing this server: the endpoint, the tool/prompt/resource names, and ready-to-paste sample prompts. Use to give someone a guided demo. For the full machine-readable capability catalog, use list_capabilities instead.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "properties": {}
  }

  arguments 5 lines copy
• list_capabilities reads open 7h ago

  List the full eDiscovery Decoder MCP surface — every tool, prompt, and resource, plus the suggested demo flow and safety boundaries — with an example prompt for each. Call this first when you are unsure which tool fits the user's question, or when tool-search shows only a partial list.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "properties": {}
  }

  arguments 5 lines copy
• calculate_elusion reads unknown 4h ago

  Estimate how much responsive/relevant material may remain in a set you chose NOT to review (the discard, null, or 'elusion' set). Use when a random sample of that excluded set has been coded — e.g. 'we sampled 400 culled docs and found 2 relevant.' Returns the elusion rate and a Wilson confidence interval. For an overall recall % from the same sample, use calculate_tar_recall_estimate. Aggregate counts only; not legal advice.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "relevant_found_in_sample",
      "sample_size"
    ],
    "properties": {
      "sample_size": {
        "type": "integer",
        "exclusiveMinimum": 0
      },
      "confidence_level": {
        "type": "number",
        "default": 0.95,
        "exclusiveMaximum": 1,
        "exclusiveMinimum": 0
      },
      "relevant_found_in_sample": {
        "type": "integer",
        "minimum": 0
      }
    },
    "additionalProperties": false
  }

  arguments 25 lines copy
• calculate_prevalence_richness reads unknown 4h ago

  Estimate how rich or prevalent a population is — the share that is responsive/relevant/positive — from positive hits in a random sample, with a Wilson confidence interval. Use for 'what % of this set is relevant?' or to size review scope and cost expectations. Aggregate counts only; not legal advice.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "positive_hits",
      "sample_size"
    ],
    "properties": {
      "sample_size": {
        "type": "integer",
        "exclusiveMinimum": 0
      },
      "positive_hits": {
        "type": "integer",
        "minimum": 0
      },
      "population_size": {
        "type": "integer",
        "exclusiveMinimum": 0
      },
      "confidence_level": {
        "type": "number",
        "default": 0.95,
        "exclusiveMaximum": 1,
        "exclusiveMinimum": 0
      }
    },
    "additionalProperties": false
  }

  arguments 29 lines copy
• calculate_review_metrics reads unknown 4h ago

  Score a coded sample when you have a full confusion matrix (true/false positives and negatives) — e.g. comparing a TAR model's calls against a reviewer's. Returns recall, precision, F1, accuracy, and in-sample elusion. Use calculate_control_set_recall if you only have relevant-found vs relevant-missed; calculate_elusion for a discard/null-set sample. Aggregate counts only; not legal advice.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "true_positives",
      "false_positives",
      "false_negatives",
      "true_negatives"
    ],
    "properties": {
      "true_negatives": {
        "type": "integer",
        "minimum": 0
      },
      "true_positives": {
        "type": "integer",
        "minimum": 0
      },
      "false_negatives": {
        "type": "integer",
        "minimum": 0
      },
      "false_positives": {
        "type": "integer",
        "minimum": 0
      }
    },
    "additionalProperties": false
  }

  arguments 29 lines copy
• calculate_tar_recall_estimate reads unknown 20h ago

  Estimate overall TAR recall and how many responsive docs were missed, by combining the responsive count already found with an elusion sample of the excluded set. Use when the user wants a recall % for the whole workflow, not just the elusion rate. For only the elusion rate and its interval, use calculate_elusion. Aggregate counts only; not legal advice.

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "responsive_found",
      "excluded_population_size",
      "elusion_responsive_hits",
      "elusion_sample_size"
    ],
    "properties": {
      "confidence_level": {
        "type": "number",
        "default": 0.95,
        "exclusiveMaximum": 1,
        "exclusiveMinimum": 0
      },
      "responsive_found": {
        "type": "integer",
        "minimum": 0
      },
      "elusion_sample_size": {
        "type": "integer",
        "exclusiveMinimum": 0
      },
      "elusion_responsive_hits": {
        "type": "integer",
        "minimum": 0
      },
      "excluded_population_size": {
        "type": "integer",
        "minimum": 0
      }
    },
    "additionalProperties": false
  }

  arguments 35 lines copy
• calculate_control_set_recall reads unknown never probed

  Calculate recall against a known control set: the share of documents already confirmed relevant that the workflow found, with a Wilson confidence interval. Use when you have relevant-found and relevant-missed counts from a fixed reference set. For recall from a confusion matrix use calculate_review_metrics; from a discard-set sample use calculate_tar_recall_estimate. Aggregate counts only; not legal advice.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "relevant_found",
      "relevant_missed"
    ],
    "properties": {
      "relevant_found": {
        "type": "integer",
        "minimum": 0
      },
      "relevant_missed": {
        "type": "integer",
        "minimum": 0
      },
      "confidence_level": {
        "type": "number",
        "default": 0.95,
        "exclusiveMaximum": 1,
        "exclusiveMinimum": 0
      }
    },
    "additionalProperties": false
  }

  arguments 25 lines copy
• validate_sample_design reads unknown never probed

  QC a TAR validation sampling plan: check whether it has the documented elements needed for a defensibility discussion (population, sample size, confidence level, sampling frame/method, randomization, etc.) and flag what is missing or inconsistent. Use to sanity-check a sampling protocol before relying on it. Reviews metadata only — not a legal sufficiency opinion.

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "population_size",
      "sample_size"
    ],
    "properties": {
      "random_seed": {
        "type": "string",
        "minLength": 1
      },
      "sample_size": {
        "type": "integer"
      },
      "generated_at": {
        "type": "string",
        "minLength": 1
      },
      "sampling_frame": {
        "type": "string",
        "minLength": 1
      },
      "margin_of_error": {
        "type": "number",
        "exclusiveMaximum": 1,
        "exclusiveMinimum": 0
      },
      "population_size": {
        "type": "integer"
      },
      "sampling_method": {
        "type": "string",
        "minLength": 1
      },
      "confidence_level": {
        "type": "number",
        "default": 0.95,
        "exclusiveMaximum": 1,
        "exclusiveMinimum": 0
      },
      "excluded_population_size": {
        "type": "integer"
      }
    },
    "additionalProperties": false
  }

  arguments 47 lines copy
• calculate_sample_size reads unknown 20h ago

  Work out how many documents to randomly sample to estimate a proportion (e.g. richness or elusion) at a target confidence level and margin of error, with finite-population correction. Use when planning a sample before review — 'how big a sample do we need?' Aggregate inputs only; not legal advice.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "population_size",
      "margin_of_error"
    ],
    "properties": {
      "margin_of_error": {
        "type": "number",
        "exclusiveMaximum": 1,
        "exclusiveMinimum": 0
      },
      "population_size": {
        "type": "integer",
        "exclusiveMinimum": 0
      },
      "confidence_level": {
        "type": "number",
        "default": 0.95,
        "exclusiveMaximum": 1,
        "exclusiveMinimum": 0
      },
      "estimated_prevalence": {
        "type": "number",
        "default": 0.5,
        "maximum": 1,
        "minimum": 0
      }
    },
    "additionalProperties": false
  }

  arguments 32 lines copy
• get_prompt_template reads unknown 15h ago

  Return the rendered text of one of this server's guided prompts (mcp-demo-tour, tar-matter-kickoff, weekly-digest). Use when the client can call tools but cannot open MCP prompts directly, or when you want to inspect a prompt's wording before using it.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "prompt_name"
    ],
    "properties": {
      "audience": {
        "type": "string",
        "minLength": 1
      },
      "week_start": {
        "type": "string",
        "pattern": "^\\d{4}-\\d{2}-\\d{2}$"
      },
      "prompt_name": {
        "enum": [
          "mcp-demo-tour",
          "tar-matter-kickoff",
          "weekly-digest"
        ],
        "type": "string"
      },
      "matter_description": {
        "type": "string",
        "minLength": 1
      }
    },
    "additionalProperties": false
  }

  arguments 30 lines copy
• get_resource_content reads unknown 15h ago

  Fetch the JSON behind a supported edd:// resource — the demo guide, TAR learning path, glossary, or news (latest / brief / by-date). Use when you want resource content but the client cannot read MCP resources directly, e.g. to pull glossary definitions or the news brief as a normal tool result.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "uri"
    ],
    "properties": {
      "uri": {
        "type": "string",
        "pattern": "^edd:\\/\\/(?:mcp\\/demo-guide|resources\\/tar-learning-path|glossary\\/core|news\\/latest|news\\/brief|news\\/\\d{4}-\\d{2}-\\d{2})$"
      }
    },
    "additionalProperties": false
  }

  arguments 14 lines copy
• compare_tar_cutoffs reads unknown 20h ago

  Compare candidate TAR score or rank cutoffs side by side: for each cutoff, how many docs sit above it, its share of the scored set, and (if responsive counts are given) an estimated precision. Use when deciding where to draw the review/cull line. Aggregate counts only; not legal advice.

  mcp-tool

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  {
    "type": "object",
    "$schema": "http://json-schema.org/draft-07/schema#",
    "required": [
      "scored_document_count",
      "cutoffs"
    ],
    "properties": {
      "cutoffs": {
        "type": "array",
        "items": {
          "type": "object",
          "required": [
            "cutoff",
            "document_count"
          ],
          "properties": {
            "label": {
              "type": "string",
              "minLength": 1
            },
            "cutoff": {
              "type": "number"
            },
            "document_count": {
              "type": "integer",
              "minimum": 0
            },
            "responsive_count": {
              "type": "integer",
              "minimum": 0
            }
          },
          "additionalProperties": false
        },
        "minItems": 1
      },
      "scored_document_count": {
        "type": "integer",
        "exclusiveMinimum": 0
      }
    },
    "additionalProperties": false
  }

  arguments 44 lines copy

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

1 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.