Product Documentation¶

Welcome to the AutoDocs MCP Server product documentation! This section contains everything you need to successfully install, configure, and use AutoDocs with your AI assistants.

🎯 What is AutoDocs MCP Server?¶

AutoDocs MCP Server is an intelligent documentation context provider that automatically supplies AI assistants with contextual, version-specific documentation for Python project dependencies. Instead of manually looking up package documentation, your AI assistant gets comprehensive context automatically.

Key Benefits¶

• 🧠 Smart Context: Automatically includes relevant dependencies with intelligent scoring
• ⚡ Fast Performance: 3-5 second response times with version-based caching
• 🛡️ Production Ready: Circuit breakers, graceful degradation, health monitoring
• 🔗 Native MCP Integration: Works seamlessly with Claude, Cursor, and other MCP clients

🚀 Quick Start¶

1. Install AutoDocs¶

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

# Using pip
pip install autodoc-mcp

2. Start the MCP Server¶

autodoc-mcp

3. Configure Your AI Client¶

Add AutoDocs to your MCP client configuration (see Installation Guide for specific clients).

4. Test with Your AI Assistant¶

Ask your AI assistant: "What packages are available in this project?"

📚 Documentation Sections¶

🌟 Featured Capabilities¶

Phase 4: Dependency Context System¶

AutoDocs goes beyond single-package documentation by providing intelligent dependency context:

• Smart Relevance Scoring: Prioritizes major frameworks (FastAPI, Django, Flask)
• Token-Aware Context: Respects AI model context limits with automatic truncation
• Concurrent Processing: Fetches multiple dependencies simultaneously
• Framework Intelligence: Special handling for Python ecosystem patterns

Example: FastAPI with Context¶

When you ask about FastAPI, AutoDocs automatically includes: - FastAPI (primary package with full documentation) - Pydantic (required for data validation) - Starlette (underlying ASGI framework) - Uvicorn (production server)

Your AI assistant gets complete context instead of fragmented information!

🎯 Use Cases¶

For AI-Assisted Development¶

• Context-Aware Code Generation: AI knows about your dependencies and their capabilities
• Accurate API Usage: Proper method signatures and parameter types
• Best Practices: Framework-specific patterns and conventions
• Error Resolution: Understanding of error messages and common fixes

For Learning & Exploration¶

• Dependency Discovery: Understand what packages your project uses
• Framework Relationships: See how packages work together
• Version Compatibility: Get documentation for your specific versions
• Architecture Understanding: Explore how components interact

📊 Performance & Reliability¶

• 277 Comprehensive Tests: Full test coverage with pytest ecosystem
• Network Resilience: Circuit breakers and exponential backoff
• Version-Based Caching: Immutable package versions cached indefinitely
• Graceful Degradation: Partial results when some dependencies fail
• Health Monitoring: Built-in health checks and performance metrics

🤝 Support & Community¶

• GitHub Issues: Report bugs or request features
• Discussions: Share use cases and get help
• Contributing: See Development Process to contribute

Next Steps¶

1. New Users: Start with Getting Started
2. Quick Setup: Jump to Installation for your MCP client
3. Power Users: Explore Configuration for advanced settings
4. Issues: Check Troubleshooting for common problems