MCP + RAG: Why I Stopped Building Complex RAG Systems After MCP Changed Everything

Short summary

After four years building complex RAG systems with embeddings and vector databases, the author replaced everything with a 150-line MCP server using simple text search and full-content delivery. LLMs extract relevance from complete context better than pre-chunked retrieval, eliminating incomplete answers. This simpler approach consistently outperformed the sophisticated RAG architecture.

• •Replaced 2,000-line complex RAG system with 150-line MCP implementation
• •Simple text search + full-content retrieval outperforms chunking/embedding strategies
• •LLMs handle context better than pre-chunked retrieval systems

Generated with AI, which can make mistakes.