docr-mcp 0.1.0

Universal MCP server for documentation with llms.txt support

docr-mcp

A framework for building MCP servers that give LLMs access to any documentation.

Give LLMs the ability to search and read documentation from any source - public or private, official or internal. Stop getting outdated answers. Start getting accurate information directly from current docs.

Why docr-mcp?

The Problem: LLMs give you outdated answers based on their training data. They don't know about the latest API changes, new features, or the specific libraries and tools you use daily.

The Solution: docr-mcp provides a proven framework to connect any documentation to LLMs through MCP servers. Give your AI assistant real-time access to current documentation - from popular libraries to your internal tools.

Key Features:

• Universal framework for any documentation site (public or private)
• Smart BM25 search with code-aware tokenization and relevance ranking
• Full customization - control parsing, indexing, search, and tool descriptions
• Production ready - 31+ tests, secure by default, proper resource management
• Easy to extend - YAML config + Python implementation to add any library

Supported Documentation

Library	Status	Install Command (Claude Code)
Strands Agents	✅ Active	claude mcp add docr-mcp-strands -- uv --directory $(pwd) run docr-mcp --library strands

Want to add a library? See CONTRIBUTING.md for guidelines.

Installation

# Clone the repository
git clone https://github.com/JacobHuang91/docr-mcp.git
cd docr-mcp

# Install dependencies
uv sync

# Add to your MCP client
# Example for Claude Code:
claude mcp add docr-mcp-strands -- \
  uv --directory $(pwd) run docr-mcp --library strands

# Restart your client to activate

Usage

After installation, ask your AI assistant to search documentation:

Search Strands docs for "agent state"
What is agent-loop in Strands?
Show me how to use model providers in Strands

How It Works

graph LR
    A[Index Source] --> B[Build BM25 Index]
    B --> C[Query]
    C --> D[Search Index]
    D --> E[Fetch Live Docs]
    E --> F[LLM Response]

1. Startup: Fetch index source (llms.txt, sitemap) and build searchable BM25 index
2. Query: Search pre-built index, then fetch live documentation from URLs
3. Search: BM25 ranking with code-aware tokenization and field weighting

License

MIT