mcp server
eDiscovery Decoder News/Calc
Free educational MCP for read-only eDiscovery news and TAR/review calculators.
Description as published by the maintainer. Source
- version 0.1.0
- active
active — Registry entry last updated 2026-06-23.
What this server can do
15 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
calculate_control_set_recall(relevant_found, relevant_missed, confidence_level)- 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. Required: relevant_found, relevant_missed.
calculate_elusion(sample_size, confidence_level, relevant_found_in_sample)- 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. Required: relevant_found_in_sample, sample_size.
calculate_prevalence_richness(sample_size, positive_hits, population_size, confidence_level)- 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. Required: positive_hits, sample_size.
calculate_review_metrics(true_negatives, true_positives, false_negatives, false_positives)- 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. Required: true_positives, false_positives, false_negatives, true_negatives.
calculate_sample_size(margin_of_error, population_size, confidence_level, estimated_prevalence)- 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. Required: population_size, margin_of_error.
calculate_tar_recall_estimate(confidence_level, responsive_found, elusion_sample_size, elusion_responsive_hits, excluded_population_size)- 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. Required: responsive_found, excluded_population_size, elusion_responsive_hits, elusion_sample_size.
compare_tar_cutoffs(cutoffs, scored_document_count)- 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. Required: scored_document_count, cutoffs.
get_demo_guide- 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.
get_news_brief(week_limit, current_limit)- 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.
get_prompt_template(audience, week_start, prompt_name, matter_description)- 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. Required: prompt_name.
get_resource_content(uri)- 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. Required: uri.
list_capabilities- 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.
ping- 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.
search_news(tags, limit, query, date_to, date_from)- 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.
validate_sample_design(random_seed, sample_size, generated_at, sampling_frame, margin_of_error, population_size, sampling_method, confidence_level, excluded_population_size)- 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. Required: population_size, sample_size.
Last successful function declaration observed on . Source: https://mcp.ediscoverydecoder.com/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://mcp.ediscoverydecoder.com/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| Latest published version | 0.1.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-06-23 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-06-23 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| mcp tools declared | 15 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | mcp.ediscoverydecoder.com | |
| mcp endpoint status | ok | The server listed 15 functions when asked. | as of probe | mcp.ediscoverydecoder.com |
Where to get it
This record as data
Every field on this page, with its source and observation date, is in the catalog JSON. Fetch the whole kind at once instead of parsing this HTML.
GET /api/v1/entries/mcp_server.json