docs-cache-mcp

A local MCP server that fetches official library documentation (llms.txt-first), caches it to disk, and serves the relevant sections to your coding agents — offline, deterministic, zero recurring cost.

By BlackRaptor AI · MIT · Companion to development-team-agents and business-council-agents.

Why

Coding agents need current, correct docs in context. Cloud docs services work, but you trade away control, offline use, and repeatability. This server keeps the whole loop local: fetch once from the official source (preferring each project's published llms.txt / llms-full.txt), cache to disk with a TTL, serve sections matched to the agent's question. When the network is down you get the cached copy, clearly flagged as stale, instead of a failure.

Related MCP server: devdocs-mcp

Quickstart

# Claude Code
claude mcp add docs-cache -- npx -y @blackraptorai/docs-cache-mcp

# or any MCP client (stdio):
npx -y @blackraptorai/docs-cache-mcp

Tools

Configuration

Ships with a default registry (Fastify, Prisma, TimescaleDB, pgvector, Anthropic SDK, AWS CDK, Playwright, React, fastify-type-provider-zod). Add or override libraries with a JSON config:

npx -y @blackraptorai/docs-cache-mcp --config ./docs-cache.config.json

{
  "libraries": [
    {
      "name": "hono",
      "urls": ["https://hono.dev/llms-full.txt", "https://hono.dev/llms.txt"],
      "ttlHours": 168,
      "description": "Hono web framework"
    }
  ]
}

URLs are candidates probed in order — list llms-full.txt first, then llms.txt, then any curated fallback page (raw GitHub READMEs work well). Cache lives at ~/.docs-cache-mcp/ (override with DOCS_CACHE_DIR). Default TTL is 7 days.

Design notes

• Offline-first: past-TTL cache is served (flagged STALE:) when the network fails — an old answer beats no answer, but the agent is told which it got.

• Index-aware: many projects publish llms.txt as a link index rather than full content. When the source looks like an index, the topic's best-matching links are fetched (and cached) one level deep.

• Deterministic retrieval: markdown heading-split + keyword scoring. No embeddings, no external calls at query time, same answer every run.

Using this in a company / behind an air gap?

This tool is free and MIT-licensed, and will stay that way. If you have a private documentation, air-gapped, or enterprise deployment need it doesn't cover — open an issue and describe your setup. Real-world reports directly shape what gets built.

Development

npm install
npm test        # vitest
npm run build   # tsc → dist/

License

MIT © 2026 Tom Hanks / BlackRaptor AI