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eDiscovery Decoder News/Calc

com.ediscoverydecoder/mcpVersion 0.1.0

Free educational MCP for read-only eDiscovery news and TAR/review calculators.

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Last checked
Nov 14, 2026, 13:55 UTC
Latency
251 ms
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Registry data as of
Nov 14, 2026, 21:12 UTC

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Connect

Streamable HTTP
https://mcp.ediscoverydecoder.com/mcpResponds251 ms
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Claude Code

claude mcp add --transport http ediscoverydecoder https://mcp.ediscoverydecoder.com/mcp

Claude Desktop and claude.ai

Settings > Connectors > Add custom connector
Name: ediscoverydecoder
Remote MCP server URL: https://mcp.ediscoverydecoder.com/mcp

Cursor

{
  "mcpServers": {
    "ediscoverydecoder": {
      "url": "https://mcp.ediscoverydecoder.com/mcp"
    }
  }
}

VS Code

code --add-mcp '{"name":"ediscoverydecoder","type":"http","url":"https://mcp.ediscoverydecoder.com/mcp"}'

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Tools

15 tools

Tools of eDiscovery Decoder News/Calc
Read-only
Idempotent
Open world
calculate_control_set_recallCalculate 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.NoYesYesNo
calculate_elusionEstimate 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.NoYesYesNo
calculate_prevalence_richnessEstimate 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.NoYesYesNo
calculate_review_metricsScore 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.NoYesYesNo
calculate_sample_sizeWork 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.NoYesYesNo
calculate_tar_recall_estimateEstimate 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.NoYesYesNo
compare_tar_cutoffsCompare 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.NoYesYesNo
get_demo_guideReturn 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.NoYesYesNo
get_news_briefGet 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.NoYesYesYes
get_prompt_templateReturn 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.NoYesYesNo
get_resource_contentFetch 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.NoYesYesNo
list_capabilitiesList 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.NoYesYesNo
pingHealth 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.NoYesYesNo
search_newsFind 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.NoYesYesYes
validate_sample_designQC 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.NoYesYesNo

Details

Registry status
Active
Published
Jun 23, 2026
Updated
Jun 23, 2026
Repository
Not given
Categories (suggested)
Search and webSuggested categories are assigned automatically from the registry text and may be wrong. Verified operators can correct them.
GDPR
Not declared

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