mcp-local-rag

mcp-local-rag

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

%%{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;

Installation

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

Using Python + uv

For this step, make sure you have uv installed: https://docs.astral.sh/uv/.

There are 2 ways to approach this:

1. Option 1: Directly running via uvx
2. Option 2: Clone and Run Locally

Run Directly via uvx

This is the easiest and quickest method. Add the following to your MCP config:

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

Clone and Run Locally

1. Clone this GitHub repository

git clone https://github.com/nkapila6/mcp-local-rag

2. Add the following to your MCP Server configuration.

{
  "mcpServers": {
    "mcp-local-rag": {
      "command": "uv",
      "args": [
        "--directory",
        "<path where this folder is located>/mcp-local-rag/",
        "run",
        "src/mcp_local_rag/main.py"
      ]
    }
  }
}

Using Docker

Ensure you have Docker installed.
Add this to your MCP server configuration:

{
  "mcpServers": {
    "mcp-local-rag": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "--init",
        "-e",
        "DOCKER_CONTAINER=true",
        "ghcr.io/nkapila6/mcp-local-rag:latest"
      ]
    }
  }
}

Security audits

MseeP does security audits on every MCP server, you can see the security audit of this MCP server by clicking here.

MCP Clients

The MCP server should work with any MCP client that supports tool calling. Has been tested on the below clients.

• Claude Desktop
• Cursor
• Goose
• Others? You try!

Examples on Claude Desktop

When an LLM (like Claude) is asked a question requiring recent web information, it will trigger mcp-local-rag.

When asked to fetch/lookup/search the web, the model prompts you to use MCP server for the chat.

In the example, have asked it about Google's latest Gemma models released yesterday. This is new info that Claude is not aware about.

Result

mcp-local-rag performs a live web search, extracts context, and sends it back to the model—giving it fresh knowledge:

Contributing

Have ideas or want to improve this project? Issues and pull requests are welcome!

License

This project is licensed under the MIT License.