MCP Local RAG
๐ ๐ - 'primitive' RAG-like web search model context protocol (MCP) server that runs locally. No APIs needed.
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 ๐ ๐ - 'primitive' rag-like web search model context protocol (mcp) server that runs locally. no apis needed.
๐ ๐ - '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
- ๐ ๐ - 'primitive' RAG-like web search model context protocol
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx mcp-local-ragConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
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
Choose your installation method
The Docker method is recommended for isolation and reproducibility. The uvx method is lighter-weight for local development.
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.
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.
Add the server config to your MCP client
Edit your claude_desktop_config.json and add the appropriate server block (Docker or uvx variant).
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 ๐ ๐ - '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.
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