mcp-local-rag

"primitive" RAG-like web search model context protocol (MCP) server that runs locally. ✨ no APIs ✨

A RAG-based web search and deep research model context protocol (MCP) server that runs entirely locally. Features multi-engine research across 9+ search backends with semantic similarity ranking, and requires no API keys.

• Features
  • Multi-Engine Deep Research
• Installation
  • Run Directly via uvx
  • Using Docker (recommended)
• Agent Skills
• Security audits
• MCP Clients
• Examples on Claude Desktop
  • Result
• Contributing
• License

%%{init: {'theme': 'base'}}%%
flowchart TD
    A[User] -->|1.Submits LLM Query| B[Language Model]
    B -->|2.Sends Query| C[mcp-local-rag Tool]
    
    subgraph mcp-local-rag Processing
    C -->|Search DuckDuckGo| D[Fetch 10 search results]
    D -->|Fetch Embeddings| E[Embeddings from Google's MediaPipe Text Embedder]
    E -->|Compute Similarity| F[Rank Entries Against Query]
    F -->|Select top k results| G[Context Extraction from URL]
    end
    
    G -->|Returns Markdown from HTML content| B
    B -->|3.Generated response with context| H[Final LLM Output]
    H -->|5.Present result to user| A

    classDef default stroke:#333,stroke-width:2px;
    classDef process stroke:#333,stroke-width:2px;
    classDef input stroke:#333,stroke-width:2px;
    classDef output stroke:#333,stroke-width:2px;

    class A input;
    class B,C process;
    class G output;

Features

Multi-Engine Deep Research

The server supports comprehensive multi-engine research capabilities that go beyond simple single-query searches:

• 9+ Search Backends: DuckDuckGo, Google, Bing, Brave, Wikipedia, Yahoo, Yandex, Mojeek, Grokipedia
• Multi-Topic Research: Search multiple related queries simultaneously
• Semantic Ranking: RAG-like similarity scoring ranks the most relevant results
• Privacy Options: Choose privacy-focused engines (DuckDuckGo, Brave) or comprehensive ones (Google)
• No API Keys Required: All processing runs locally with embedded models

Deep Research Tools

1. deep_research - Comprehensive multi-engine research

   • Search across multiple engines simultaneously
   • Ideal for complex topics requiring diverse perspectives
   • Customizable backends and result limits

2. deep_research_google - Google-focused deep dive

   • Leverage Google's comprehensive index
   • Best for technical/scientific queries

3. deep_research_ddgs - Privacy-first deep research

   • Use DuckDuckGo for private, extensive research
   • Great for general topics without tracking

4. rag_search_ddgs & rag_search_google - Quick single searches

   • Fast, focused searches when you need quick answers

Installation

Locate your MCP config path here or check your MCP client settings.

Run Directly via uvx

This is the easiest and quickest method. You need to install uv for this to work.
Add this to your MCP server configuration:

{
  "mcpServers": {
    "mcp-local-rag":{
      "command": "uvx",
        "args": [
          "--python=3.10",
          "--from",
          "git+https://github.com/nkapila6/mcp-local-rag",
          "mcp-local-rag"
        ]
      }
  }
}

Using Docker (recommended)

Ensure you have Docker installed.<

... View full README on GitHub