MCP Local RAG

v1.0.0โ€ขSearch & Data Extractionโ€ขstable

๐Ÿ  ๐Ÿ - 'primitive' RAG-like web search model context protocol (MCP) server that runs locally. No APIs needed.

mcpmcp-servermodel-context-protocolragweb-seach
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What is MCP Local RAG?

MCP Local RAG is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to ๐Ÿ  ๐Ÿ - 'primitive' rag-like web search model context protocol (mcp) server that runs locally. no apis needed.

๐Ÿ  ๐Ÿ - 'primitive' RAG-like web search model context protocol (MCP) server that runs locally. No APIs needed.

This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.

Features

  • ๐Ÿ  ๐Ÿ - 'primitive' RAG-like web search model context protocol

Use Cases

Perform local RAG-like web search without API dependencies.
Enable offline semantic search for local document retrieval.
nkapila6

Maintainer

LicenseMIT License
Languagepython
Versionv1.0.0
UpdatedMay 18, 2026
Statushealthy
Maintenanceactive

Works with

ClaudeOpenAIwindowsmacoslinux

Installation

Manual Installation

npx mcp-local-rag

Configuration

Configuration Details

Config File

claude_desktop_config.json

Performance

Response Metrics

Response Time< 200ms
ThroughputMedium

Resource Usage

Memory UsageLow
CPU UsageLow

How to Set Up and Use MCP Local RAG

MCP Local RAG is a privacy-first, API-key-free web search MCP server that performs RAG-like retrieval across 9+ search backends โ€” including DuckDuckGo, Google, Bing, Brave, Wikipedia, Yahoo, Yandex, Mojeek, and Grokipedia โ€” all running locally on your machine. It exposes tools for deep multi-engine research and quick single-engine queries, making it useful for AI assistants that need web search capabilities without sending data to a third-party search API. Because it requires no external API keys and can run inside Docker, it is especially suited for air-gapped or privacy-conscious environments.

Prerequisites

  • Python 3.10 or later
  • Docker (recommended installation method) or uv package manager (for uvx method)
  • An MCP-compatible client such as Claude Desktop or Claude Code
  • No external API keys required
1

Choose your installation method

The Docker method is recommended for isolation and reproducibility. The uvx method is lighter-weight for local development.

2

Configure via Docker (recommended)

Add the Docker-based server entry to your MCP client configuration. No build step required โ€” it pulls the prebuilt image from the GitHub Container Registry.

3

Or configure via uvx

If you prefer not to use Docker, configure the server using uvx with Python 3.10 and the GitHub source directly.

4

Add the server config to your MCP client

Edit your claude_desktop_config.json and add the appropriate server block (Docker or uvx variant).

5

Restart your MCP client and test

Restart Claude Desktop and ask it to search the web for a topic. The server will fan out the query across multiple backends and return consolidated results.

MCP Local RAG Examples

Client configuration (Docker)

Docker-based configuration. Uses the prebuilt image from GitHub Container Registry with DOCKER_CONTAINER=true so the server knows it is running inside a container.

{
  "mcpServers": {
    "mcp-local-rag": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i", "--init",
        "-e", "DOCKER_CONTAINER=true",
        "ghcr.io/nkapila6/mcp-local-rag:v1.0.2"
      ]
    }
  }
}

Client configuration (uvx)

Alternative configuration using uvx to run directly from the GitHub repository without Docker.

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

Prompts to try

Web research prompts that use the server's multi-engine search tools.

- "Do deep research on the latest developments in quantum error correction"
- "Search DuckDuckGo for privacy-preserving machine learning techniques"
- "Find comprehensive documentation about Kubernetes network policies from multiple sources"
- "Research sustainable energy storage solutions across several search engines"
- "Use Wikipedia and Google to give me background on the history of RISC-V"

Troubleshooting MCP Local RAG

Docker image fails to pull or run

Ensure Docker Desktop is running and you are connected to the internet for the initial image pull. Verify the image tag (v1.0.2) is current by checking the GitHub packages page at ghcr.io/nkapila6/mcp-local-rag. You can also pull the image manually first: 'docker pull ghcr.io/nkapila6/mcp-local-rag:v1.0.2'.

uvx installation fails with Python version errors

The server requires Python 3.10 specifically. Make sure Python 3.10 is installed on your system ('python3.10 --version') and that uv can find it. If using pyenv, run 'pyenv install 3.10' and ensure it is available in PATH.

Search returns no results or times out

Some search backends (Bing, Google) may rate-limit automated requests. Try the DuckDuckGo-specific tool (deep_research_ddgs or rag_search_ddgs) as it tends to be more permissive. If running inside Docker on a corporate network, ensure the container has outbound internet access.

Frequently Asked Questions about MCP Local RAG

What is MCP Local RAG?

MCP Local RAG is a Model Context Protocol (MCP) server that ๐Ÿ  ๐Ÿ - 'primitive' rag-like web search model context protocol (mcp) server that runs locally. no apis needed. It connects AI assistants to external tools and data sources through a standardized interface.

How do I install MCP Local RAG?

Follow the installation instructions on the MCP Local RAG GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.

Which AI clients work with MCP Local RAG?

MCP Local RAG works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.

Is MCP Local RAG free to use?

Yes, MCP Local RAG is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.

Browse More Search & Data Extraction MCP Servers

Explore all search & data extraction servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.

Quick Config Preview

{ "mcpServers": { "mcp-local-rag": { "command": "npx", "args": ["-y", "mcp-local-rag"] } } }

Add this to your claude_desktop_config.json or .cursor/mcp.json

Read the full setup guide โ†’

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