The AutoDocs MCP Server provides intelligent documentation context for Python projects and their dependencies, optimized for AI assistants.
Scan Project Dependencies: Automatically scan and analyze project dependencies from pyproject.toml files, returning project metadata and dependency specifications.
Retrieve Contextual Documentation: Fetch comprehensive, version-specific documentation for Python packages, intelligently including up to 8 most relevant runtime dependencies while respecting AI context window token limits.
Cache Management: Clear the local documentation cache or retrieve detailed statistics about cache contents, performance metrics, and hit rates.
Health Monitoring: Check overall system health status and perform readiness checks for deployment purposes.
Performance Metrics: Access detailed system performance data, including request/response statistics and cache performance analytics.
AI-Optimized: Provides structured JSON outputs, token-aware context management, and smart dependency resolution with network resilience for scalable use in development and production environments.
Retrieves package information and documentation from PyPI for Python project dependencies, enabling automatic documentation lookup based on resolved package versions.
Provides contextual, version-specific documentation for Python project dependencies by parsing pyproject.toml files and extracting dependency information.
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.
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
Add to your MCP client configuration:
📚 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
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
AutoDocs provides 8 production-ready MCP 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
refresh_cache - Clear documentation cache
get_cache_stats - View cache statistics
health_check - Comprehensive system health status
ready_check - Kubernetes-style readiness check
get_metrics - Performance metrics and monitoring data
This project welcomes contributions! Please see our Contributing Guide for detailed information.
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
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
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
MIT License - see LICENSE for details.
Built with
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
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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