docs-cache-mcp
A local MCP server that fetches official library documentation (llms.txt-first), caches it to disk, and serves relevant sections to coding agents offline with deterministic retrieval.
README
VibeCTX
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.
Published on npm as
@blackraptorai/vibectx. (Formerly@blackraptorai/docs-cache-mcp≤ 0.1.1 — deprecated in favor of this package.)
By BlackRaptor AI · MIT · Companion to BlackRaptor Agents — development and BlackRaptor Agents — council.
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.
Quickstart
# Claude Code
claude mcp add vibectx -- npx -y @blackraptorai/vibectx
# or any MCP client (stdio):
npx -y @blackraptorai/vibectx
Tools
| Tool | What it does |
|---|---|
list_libraries() |
Registry + per-library cache status |
get_docs(library, topic?, maxTokens?) |
Fetch-or-cache, then return the sections best matching topic (follows llms.txt index links when needed). No topic → table of contents + document head |
refresh(library?) |
Force refetch past the TTL (all libraries when omitted) |
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/vibectx --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.txtas 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
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