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Free educational MCP for read-only eDiscovery news and TAR/review calculators.
Remote server: your requests and data go to mcp.ediscoverydecoder.com. Claude Code: claude mcp add --transport http calc https://mcp.ediscoverydecoder.com/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
ping — Health check: confirm the eDiscovery Decoder News/Calc MCP server is reachable before a demo or when troubleshooting a connection. Returns slist_capabilities — List the full eDiscovery Decoder MCP surface — every tool, prompt, and resource, plus the suggested demo flow and safety boundaries — with aget_demo_guide — Return a short, human-readable walkthrough for testing this server: the endpoint, the tool/prompt/resource names, and ready-to-paste sample get_resource_content — Fetch the JSON behind a supported edd:// resource — the demo guide, TAR learning path, glossary, or news (latest / brief / by-date). Use wheget_prompt_template — 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 search_news — 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 get_news_brief — Get the current eDiscovery Decoder news brief: top stories plus a Week in Review breakdown, returned both as structured data and as display-calculate_review_metrics — Score a coded sample when you have a full confusion matrix (true/false positives and negatives) — e.g. comparing a TAR model's calls againstcalculate_elusion — Estimate how much responsive/relevant material may remain in a set you chose NOT to review (the discard, null, or 'elusion' set). Use when acalculate_sample_size — Work out how many documents to randomly sample to estimate a proportion (e.g. richness or elusion) at a target confidence level and margin ocalculate_tar_recall_estimate — Estimate overall TAR recall and how many responsive docs were missed, by combining the responsive count already found with an elusion samplecalculate_prevalence_richness — Estimate how rich or prevalent a population is — the share that is responsive/relevant/positive — from positive hits in a random sample, witcalculate_control_set_recall — Calculate recall against a known control set: the share of documents already confirmed relevant that the workflow found, with a Wilson conficompare_tar_cutoffs — 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 validate_sample_design — QC a TAR validation sampling plan: check whether it has the documented elements needed for a defensibility discussion (population, sample siNot tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.