llmsdottxt-mcp 0.1.1

Auto-discover llms.txt from your deps. Docs your agent can read. Zero setup.

llmsdottxt-mcp

Auto-discover llms.txt from your deps. Docs your agent can read. Zero setup.

llmsdottxt-mcp scans your project's dependencies, discovers each package's llms.txt documentation endpoint, fetches and indexes the content locally, and exposes it to AI coding agents over the Model Context Protocol — so your agent reads first-party docs instead of guessing or scraping.

Why

Unlike scraping-based doc tools, llmsdottxt-mcp is local-first and llms.txt-native: auto-discovery from your real dependencies, platform-aware fetching (Mintlify, Read the Docs, Docusaurus, GitHub Pages), a persistent searchable index, and size-safe handling of large llms-full.txt files.

It is also resilient to docs hosts that push back: a genuine 429/503 is retried with backoff (honoring Retry-After), while an unsolvable bot challenge — Cloudflare, DataDome, Imperva, AWS WAF, Akamai, Sucuri — is detected and reported as blocked (a browser is required to pass it), so it is never silently miscounted as "no docs".

Status

• Project status: pre-1.0 public preview.
• Python: 3.14+.
• API stability: MCP tool names and response schemas may change before 1.0.
• Support: GitHub issues for bugs and features; private security reports for vulnerabilities.

Install & Run

uvx llmsdottxt-mcp scan      # index the current project's dependencies
uvx llmsdottxt-mcp status    # show indexed packages
uvx llmsdottxt-mcp serve     # start the MCP server on stdio
uvx llmsdottxt-mcp doctor    # diagnose paths, write access, registry connectivity

Add it to an MCP client (e.g. Claude Code) as a stdio server running llmsdottxt-mcp serve.

MCP Surface

Tools: index_deps, search, browse, status. Resources: llmstxt://packages, llmstxt://package/{ecosystem}/{name}. Prompts: find_docs_for_import.

Configuration

Everything is convention-based; override via LLMSTXT_* env vars (see .env.example). The index lives in ~/.llms.txt.d/.

Ecosystems

Four ecosystems are supported end-to-end — each pairs a manifest scanner with a registry resolver:

Ecosystem	Manifest(s)	Registry	Docs source
Python	pyproject.toml, requirements.txt	PyPI JSON API	project URLs / homepage
Node	package.json	npm registry	homepage
Rust	Cargo.toml	crates.io API	documentation / homepage
Go	go.mod	proxy.golang.org	pkg.go.dev

Add an ecosystem by dropping a BaseScanner and BaseResolver into their registries — see ROADMAP.md.

Development

uv sync --all-groups
uv run ruff check && uv run ty check && uv run basedpyright
uv run lint-imports && uv run pytest -n auto

See AGENTS.md for architecture and conventions.