AutoDocs MCP Server

Intelligent documentation context provider for AI assistants

AutoDocs MCP Server automatically provides AI assistants with contextual, version-specific documentation for Python project dependencies. It uses intelligent dependency resolution to include both the requested package and its most relevant dependencies, giving AI assistants comprehensive context for accurate code assistance.

Key Features

Smart dependency context - Automatically includes 3-8 most relevant dependencies
AI-optimized documentation - Token-aware formatting with performance metrics
Production-ready - 8 MCP tools with health monitoring and caching
Framework-aware - Special handling for FastAPI, Django, Flask ecosystems
High performance - Concurrent fetching with circuit breakers and connection pooling

Quick Start

Installation

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

# Using pip
pip install autodoc-mcp

Basic Usage

# Start the MCP server
autodoc-mcp

# Test with your AI assistant
# Ask: "What packages are available in this project?"
# Ask: "Tell me about FastAPI with its dependencies"

MCP Client Configuration

Add to your MCP client configuration:

{
  "mcpServers": {
    "autodoc-mcp": {
      "command": "autodoc-mcp",
      "env": {
        "CACHE_DIR": "~/.cache/autodoc-mcp"
      }
    }
  }
}

Documentation

📚 Complete Documentation: https://bradleyfay.github.io/autodoc-mcp/

Our documentation is organized into three focused paths:

Product Documentation - Installation, configuration, API reference, and troubleshooting
Development Process - Architecture, contributing guidelines, and development standards
Development Journey - Project evolution and AI-assisted development insights

Quick Links

Installation Guide - Setup for different platforms and MCP clients
MCP Tools Reference - Complete API documentation for all 8 tools
Configuration Options - Environment variables and advanced settings
Troubleshooting Guide - Common issues and solutions
Contributing Guide - How to contribute to the project

MCP Tools Overview

AutoDocs provides 8 production-ready MCP tools:

Core Tools

get_package_docs_with_context - Primary tool for comprehensive documentation with dependencies
scan_dependencies - Parse project dependencies from pyproject.toml
get_package_docs - Legacy single-package documentation tool

Cache Management

refresh_cache - Clear documentation cache
get_cache_stats - View cache statistics

System Health

health_check - Comprehensive system health status
ready_check - Kubernetes-style readiness check
get_metrics - Performance metrics and monitoring data

Development & Contributing

This project welcomes contributions! Please see our Contributing Guide for detailed information.

Quick Development Setup

git clone https://github.com/bradleyfay/autodoc-mcp.git
cd autodoc-mcp
uv sync --all-extras
uv run pytest  # Run 400+ tests

Development Standards

Conventional Commits - All commits must follow conventional commit format
Pre-commit Hooks - Automated linting, formatting, and type checking
Comprehensive Testing - pytest ecosystem with 400+ tests
GitFlow Workflow - Feature branches, release branches, and semantic versioning

Project Information

Version: 0.5.1 (Production Ready)
Python: 3.11+ required
License: MIT
Architecture: Layered design with 10 specialized core service modules
Dependencies: Minimal production footprint with FastMCP, httpx, Pydantic

Transparency & Learning

This project demonstrates transparent AI-assisted development. Explore these directories to see the complete development process:

.claude/agents/ - Claude Code agent configurations
.specstory/history/ - Complete session history
planning/ - Planning documents and technical decisions

License

MIT License - see LICENSE for details.

Built with FastMCP | Documentation Site | GitHub

Deploy Server

HTTP connection URL

security – no known vulnerabilities

license - permissive license

quality - confirmed to work

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

Automatically provides AI assistants with contextual, version-specific documentation for Python project dependencies by scanning pyproject.toml files. Eliminates manual package lookup and enables more accurate coding assistance through seamless integration with AI tools.

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