autodoc-mcp 0.1.7

MCP server for automatic Python package documentation context

AutoDocs MCP Server

AutoDocs MCP Server automatically provides AI assistants with contextual, version-specific documentation for Python project dependencies, eliminating manual package lookup and providing more accurate coding assistance.

Features

• Automatic Dependency Scanning: Parse pyproject.toml files and extract dependency information
• Version-Specific Caching: Cache documentation based on resolved package versions
• Graceful Degradation: Handle malformed dependencies and network issues gracefully
• Rich Context: Provide AI assistants with both primary package and dependency documentation
• FastMCP Integration: Built with FastMCP for seamless integration with AI tools like Cursor

Installation

# Using uv (recommended)
uv tool install autodocs-mcp

# Using pip
pip install autodocs-mcp

Usage

As an MCP Server

Configure in your Cursor Desktop settings:

{
  "mcpServers": {
    "autodocs-mcp": {
      "command": "uv",
      "args": ["run", "--from", "autodocs-mcp", "autodocs-mcp"],
      "env": {
        "AUTODOCS_CACHE_DIR": "/path/to/cache"
      }
    }
  }
}

Development

# Install dependencies
uv sync --all-extras

# Run tests
uv run pytest

# Run linting
uv run ruff check

# Start development server
uv run hatch run dev

MCP Tools

scan_dependencies

Scans project dependencies from pyproject.toml files.

Parameters:

• project_path (optional): Path to project directory (defaults to current directory)

Returns:

• Project metadata and dependency specifications
• Graceful degradation information for malformed dependencies

get_package_docs (Coming Soon)

Retrieves formatted documentation for Python packages.

Configuration

Environment variables:

• AUTODOCS_CACHE_DIR: Cache directory location (default: ~/.autodocs/cache)
• AUTODOCS_MAX_CONCURRENT: Maximum concurrent PyPI requests (default: 10)
• AUTODOCS_REQUEST_TIMEOUT: Request timeout in seconds (default: 30)
• AUTODOCS_LOG_LEVEL: Logging level (default: INFO)

Architecture

• FastMCP Server: Handles MCP protocol communication
• Dependency Parser: Parses pyproject.toml with graceful error handling
• Documentation Fetcher: Retrieves package info from PyPI (coming soon)
• Cache Manager: Version-based caching system (coming soon)

Development Status

This is currently in Priority 1: Core Validation phase:

• ✅ Basic project setup with hatch/uv
• ✅ Minimal viable dependency parser
• ✅ Basic FastMCP integration
• ✅ scan_dependencies MCP tool
• 🚧 Testing with real projects

Coming next: Documentation fetching, version-based caching, and rich dependency context.

License

MIT