papermoon-mkdocs-mcp

A lightweight MCP server for MkDocs documentation sites. Reads markdown files directly from disk, provides full-text and optional semantic search, and exposes project structure through the Model Context Protocol.

Features

• 5 MCP tools -- search, read_document, list_documents, get_project_info, get_document_outline

• SQLite FTS5 keyword search with BM25 ranking (zero external dependencies)

• Optional semantic vector search via sentence-transformers

• Hybrid search combining keyword + vector results with Reciprocal Rank Fusion

• Incremental indexing -- fast updates when files change

• Persistent SQLite index that survives server restarts

• Navigation-aware -- parses mkdocs.yml and .nav.yml

• Security-first -- path traversal prevention, read-only search connections

• Minimal dependencies -- 3 required, 2 optional

Related MCP server: mdbook-mcp-server

Installation

pip install papermoon-mkdocs-mcp

To enable vector search:

pip install papermoon-mkdocs-mcp[vector]

Quick Start

Run from the root of any MkDocs project (where mkdocs.yml lives):

cd /path/to/your/mkdocs-project
papermoon-mkdocs-mcp

Or point to a specific config file:

papermoon-mkdocs-mcp --config /path/to/mkdocs.yml

The server auto-detects mkdocs.yml in the current directory when --config is omitted.

Transport Options

By default the server uses stdio transport. You can switch to a network transport for remote or multi-client setups:

# Streamable HTTP (recommended for network access)
mkdocs-mcp --transport streamable-http --host 0.0.0.0 --port 9000

# SSE (legacy client compatibility)
mkdocs-mcp --transport sse --port 8080

Security note: When binding to a non-loopback address, place the server behind a reverse proxy (e.g. nginx, Caddy) that terminates TLS.

MCP Client Configuration

Claude Desktop

Add to your Claude Desktop configuration file:

{
  "mcpServers": {
    "mkdocs": {
      "command": "papermoon-mkdocs-mcp",
      "args": ["--config", "/path/to/mkdocs.yml"]
    }
  }
}

Note: If Claude Desktop can't find the command (Failed to spawn process: No such file or directory), use the full path to the executable instead of just mkdocs-mcp:

{
  "mcpServers": {
    "mkdocs": {
      "command": "/path/to/.venv/bin/mkdocs-mcp",
      "args": ["--config", "/path/to/mkdocs.yml"]
    }
  }
}

This is common when the package is installed in a virtual environment whose bin/ directory isn't in Claude Desktop's PATH.

Claude Code / VS Code

Add to .mcp.json in your project root:

{
  "mcpServers": {
    "mkdocs": {
      "command": "papermoon-mkdocs-mcp",
      "args": ["--config", "/path/to/mkdocs.yml"]
    }
  }
}

Available Tools

search

Search documentation using keyword, semantic, or hybrid search.

Returns ranked results with path, title, relevance score (normalized 0.0--1.0), and text snippet.

read_document

Read a documentation file by its relative path.

Returns the markdown body (frontmatter stripped), parsed frontmatter as a separate field, heading structure, and file metadata.

list_documents

List all documentation files, optionally filtered by section.

Returns document metadata (path, title, description, categories, size, mtime).

get_project_info

Get MkDocs project metadata. Takes no parameters.

Returns site name, site URL, docs directory, theme, navigation tree, document count, and index status.

get_document_outline

Get the heading structure (table of contents) for a document.

Returns the document title and a list of headings with level, text, and anchor.

Architecture

src/mkdocs_mcp/
  config.py      -- MkDocs config detection and nav parsing
  repository.py  -- SQLite schema and CRUD operations
  indexer.py     -- Index orchestration with incremental updates
  searcher.py    -- Keyword, vector, and hybrid search
  server.py      -- FastMCP server with 5 tool definitions
  utils.py       -- Path validation, frontmatter parsing, text extraction
  models.py      -- Pydantic response models

At startup the server reads mkdocs.yml, scans the docs directory, and builds (or incrementally updates) a SQLite FTS5 index. Search queries hit the index directly; vector search embeds the query with all-MiniLM-L6-v2 and compares against stored document embeddings. Hybrid mode fuses both result lists using Reciprocal Rank Fusion.

Development

git clone https://github.com/aspect-build/mkdocs-mcp.git
cd mkdocs-mcp
pip install -e ".[dev]"
pytest

Linting and type checking:

ruff check .
mypy src/

Requirements

• Python >= 3.10

• Required: fastmcp (>=3.0, <4), pydantic (>=2.0, <3), pyyaml (>=6.0), markdown (>=3.4)

• Optional (vector search): sentence-transformers (>=3.0), numpy (>=1.24)

License

See LICENSE for details.