I Built the Ultimate RAG MCP Server for AI Coding (Better than Context7)
AI Summary
Summary of Video: Context 7 and Crawl for AI
- Introduction to AI Coding Assistants
- AI coding assistants can build applications but have limitations with specific tools and frameworks.
- The need for integrating RAG (retrieval-augmented generation) capabilities into AI coding assistants.
- Context 7 Overview
- A free MCP server that provides RAG knowledge bases for various frameworks (e.g., Superbase, MCP).
- Allows users to search for documentation efficiently, reducing hallucinations in coding.
- Context 7 provides access to documentation for over 8,000 libraries but is restricted to a collective knowledge base, which may not suit all users.
- Limitations of Context 7
- Users often need only a small subset of documentation, leading to irrelevant results.
- Private repositories cannot be integrated into the knowledge base.
- Core functions are not open-source, raising concerns about future monetization.
- Crawl for AI Project
- A new mission to create an open-source RAG MCP server allowing users to build private knowledge bases.
- Includes scraping capabilities to gather documentation from custom sources, making it adaptable to specific tech stacks.
- Demonstrated setup in Windsurf, showcasing ease of integrating Pyantic AI and Mem Zero for knowledge management.
- Technical Implementation
- Detailed instructions for crawling documentation websites and scraping specific pages.
- Demonstrated how to configure the server and retrieve documentation dynamically to feed AI coding assistants.
- Discussed future improvements, such as enhanced RAG strategies, faster crawling, and the potential for private embedding models.
- Conclusion
- Encourages viewers to use the MCP server and share feedback for further development.