llmdocs-mcp 0.1.1

Agent-first documentation platform (CLI and server)

llmdocs

Agent-first documentation platform — self-hosted, MCP-native, no external vector API required.

• MCP tools via Streamable HTTP (/mcp) for AI agents and IDEs
• Hybrid search — Chroma semantic + BM25 keyword fusion
• Raw markdown URLs — GET /guide.md returns clean content, no frontmatter
• llms.txt generation (coming soon)
• Embedded Chroma — no external vector DB needed

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Quickstart (CLI)

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt && pip install .
llmdocs --help

The llmdocs CLI is the only supported interface. Internal Python modules are implementation details and may change without notice.

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Development

python -m venv .venv && source .venv/bin/activate
pip install -r requirements-dev.txt
pip install -e .
pytest

Git note: this repo is rooted in Projects/llmdocs. If your editor opened a parent directory that is also a Git repo, run git commands from here so commits apply only to llmdocs.

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Server routes

Route	Description
GET /	JSON metadata and endpoint index
GET /health	{"status": "healthy"}
POST /mcp	Streamable HTTP MCP endpoint (FastMCP)
GET /<path>.md	Raw markdown body, no frontmatter

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MCP tools

Connect any MCP-compatible client (Cursor, Claude, etc.) to http://localhost:8080/mcp.

Tool	Description
search_docs	Hybrid semantic + keyword search over indexed chunks
get_doc	Fetch full document body and metadata by path
list_docs	List all documents, optionally filtered by category or path prefix

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Package layout

llmdocs/
  cli.py          User entry point (Click commands)
  config.py       llmdocs.yaml loading
  models.py       Internal types (Document, Chunk, SearchResult)
  doc_paths.py    Safe URL → filesystem resolution
  server.py       FastAPI app + lifespan startup indexing

  indexing/       Indexing pipeline
    parser.py     Frontmatter + markdown loading
    chunker.py    H2/H3 section chunking
    hasher.py     SHA-256 file hashing for incremental indexing
    indexer.py    Chroma store + sentence-transformers embeddings
    search.py     HybridSearchEngine (semantic + BM25)

  mcp/            MCP layer (FastMCP, Streamable HTTP at /mcp)
    __init__.py   FastMCP server singleton + tool registrations
    runtime.py    LlmdocsRuntime — state shared between lifespan and tools
    tools.py      Tool logic (pure functions)

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Docker

The production image installs requirements.txt then pip install .. A Dockerfile and docker-compose.yml will be added in a later task.

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License

MIT