MCP Local RAG

The MCP Local RAG is a powerful, local-first retrieval-augmented generation (RAG) server specifically designed for developers using the Model Context Protocol (MCP). This innovative solution enables seamless semantic and keyword-based search for code and technical documents, ensuring that privacy and operational simplicity are prioritized.

Key Features

• Semantic Search with Keyword Boost
  Experience powerful vector search capabilities that prioritize exact matches of technical terms like useEffect and error codes, while still maintaining semantic relevance. This hybrid approach guarantees that users find exactly what they are looking for, efficiently.

• Smart Semantic Chunking
  Documents are intelligently chunked based on meaning rather than arbitrary character counts. This ensures related content remains unified while distinct topics are effectively separated for optimal comprehension.

• Quality-First Result Filtering
  Results are grouped by relevance gaps rather than fixed top-K thresholds, leading to fewer but more trustworthy search outcomes.

• Runs Entirely Locally
  All operations take place on the user's machine, eliminating the need for API keys or cloud services, which keeps sensitive data secure and enhances confidentiality.

• Zero-Friction Setup
  The solution is designed for an effortless user experience, requiring only a single command via npx, with no need for Docker, Python, or server management.

Quick Start

Setting up the server is straightforward. Simply set the BASE_DIR to the folder of your document and integrate with various AI coding tools, such as Cursor, Codex, and Claude Code:

{
  "mcpServers": {
    "local-rag": {
      "command": "npx",
      "args": ["-y", "mcp-local-rag"],
      "env": {
        "BASE_DIR": "/path/to/your/documents"
      }
    }
  }
}

Use Cases

This RAG server is particularly useful for searching through:

• Technical specifications
• Research papers
• Internal documentation

It provides a comprehensive set of tools including ingesting files, searching, managing document lists, and status checks, enabling developers to interact efficiently with their documentation.

Why MCP Local RAG?

While many existing solutions route documents to external APIs for processing, MCP Local RAG prioritizes privacy by operating entirely offline after the initial model download. This not only protects sensitive information but also eliminates usage costs associated with external embedding APIs. Unlike purely semantic search methods, the keyword boost feature captures both imprecise and exact matches, enhancing the effectiveness of code searches.

Performance Specs

In terms of performance, MCP Local RAG has been fine-tuned to handle multiple concurrent queries efficiently, making it suitable for large document sets. The ingestion process is also optimized to minimize overhead, allowing for rapid parsing and embedding without sacrificing quality.

Conclusion

MCP Local RAG stands out by offering a practical, privacy-first approach to searching technical documentation. With features aimed at developers' needs, it combines semantic intelligence with operational simplicity, setting a new standard for local-first document management in software development.

Explore the MCP Local RAG GitHub Repository for more details and to get started.