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agentdocs

agentdocs

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github.com/titov-dDeveloper Tools0Updated 4w ago
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About agentdocs

agentdocs is a local MCP server for coding agents building MCP servers and Claude agents. It provides human-reviewed, source-verified documentation with links to upstream references.

Key features

  1. 01Browse topics and verification status
  2. 02Search ranked documentation snippets
  3. 03Read complete Markdown documents
  4. 04Show upstream source links
  5. 05Track human review and verification dates

Use cases

  1. 01Look up MCP implementation details
  2. 02Check Claude Agent SDK usage
  3. 03Verify sources and read full documents

MCP support

It runs locally over stdio and exposes topic listing, document search, and document retrieval tools to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "titov-d-agentdocs": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-agentdocs"
      ]
    }
  }
}
Run commandnpx -y mcp-agentdocs

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmmcp-agentdocs0.1.3

Tools

3

Saved tool list · updated 2w ago

This tool takes no parameters.

Details

From the project README

agentdocs

Fresh, source-verified docs for building MCP servers and Claude agents — served straight to your AI coding agent over MCP.

npm license: MIT Model Context Protocol

Coding agents hallucinate on fast-moving APIs like the Model Context Protocol and the Claude Agent SDK: training data lags the spec, so you get plausible-but-wrong imports, deprecated patterns, and invented options. agentdocs is a local MCP server that gives your agent a curated, source-verified corpus to search instead of guessing.

  • 🔎 Three tools: list_topics, search_docs, get_doc
  • 📎 Every answer is attributed to its upstream source URL
  • 🏠 Runs locally over stdio — no account, no hosting, no data leaves your machine
  • ✅ Each doc carries a verified flag set only after human review against the source

Install & connect

Requires Node.js 18+.

Claude Code
claude mcp add agentdocs -- npx -y mcp-agentdocs@latest
Cursor / other MCP clients (mcp.json)
{
  "mcpServers": {
    "agentdocs": {
      "command": "npx",
      "args": ["-y", "mcp-agentdocs@latest"]
    }
  }
}

Then ask your agent things like "check agentdocs for how MCP stdio handles logging" or "search agentdocs for the Streamable HTTP session header" — it will call the tools and answer from the verified corpus.

Tools

ToolUse it to
list_topicsSee every curated doc, its topic, source URL, and verified status
search_docs(query, topic?)Get ranked, source-attributed snippets for a question
get_doc(id)Read the full verified markdown of one document

What's covered (v1)

The Model Context Protocol (spec 2025-11-25) and building MCP servers / Claude agents. Corpus grows over time; run list_topics for the current set.

Freshness

Every document records the date it was last verified against its primary source. Sources are diffed on a regular cadence; when one drifts, the affected doc is re-verified and a new version is published. Using mcp-agentdocs@latest (as above) keeps you on the current corpus. See CHANGELOG.md for what changed and when.

Development

npm install
npm run build      # tsc -> dist/
npm test           # vitest: retrieval, manifest integrity, unicode safety
npm run ingest     # refresh sources into corpus/_staging/ for human review

Curation is the point: npm run ingest fetches upstream sources for review but never silently overwrites a curated doc — a human diffs, promotes, and sets verified: true in corpus/manifest.json only after checking the content against its source. Run npm run check-freshness to flag docs that have drifted or are due for re-verification.

License

MIT

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.