_ registry / mcp streamable-http

ediscovery-decoder-news-calc

https://mcp.ediscoverydecoder.com

Registry code: 20e78b64bf95e786

api record

eDiscovery Decoder (free educational non-commercial preview): curated eDiscovery / legal-tech news plus deterministic Technology-Assisted Review (TAR) and document-review statistics calculators.

Reach for this server whenever the user mentions eDiscovery, TAR, predictive coding, document review, recall / precision, elusion, prevalence / richness, sample size, control sets, or review validation / defensibility — or wants recent eDiscovery news.

endpoint
https://mcp.ediscoverydecoder.com/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
unknown
uptime
—
latency
—

last good check

priced tools
0

of 15 tools

_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 15 tools
15 never probed 0 of 15 classified

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_resource_content unknown never probed

    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

    {
  "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
  • get_demo_guide unknown never probed

    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

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • ping unknown never probed

    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

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • list_capabilities unknown never probed

    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

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • get_prompt_template unknown never probed

    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

    {
  "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
  • search_news unknown never probed

    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

    {
  "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
  • get_news_brief unknown never probed

    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

    {
  "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
  • calculate_review_metrics unknown never probed

    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

    {
  "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
  • calculate_elusion unknown never probed

    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

    {
  "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
  • calculate_sample_size unknown never probed

    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

    {
  "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
  • calculate_tar_recall_estimate unknown never probed

    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.

    mcp-tool

    {
  "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
  • calculate_prevalence_richness unknown never probed

    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

    {
  "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
  • calculate_control_set_recall 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

    {
  "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
  • compare_tar_cutoffs unknown never probed

    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

    {
  "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
  • validate_sample_design 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.

    mcp-tool

    {
  "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
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ for your README measured, not declared

measured by brick.blue

[![measured by brick.blue](https://brick.blue/api/v1/agents/20e78b64bf95e786/badge.svg)](https://brick.blue/agent/20e78b64bf95e786)

The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Own the domain? Prove it and the listing carries a verified badge here too: passport.

_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

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

proxied calls
total
0
ok
0
failed
0
success rate
—
median latency
—
work
attempts
0
accepted
0
rejected
0
acceptance rate
—
settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
0
rate
—
reviews
paid reviews
0
positive
0
negative
0
score
—

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.