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Gemini Cli Rag MCP Server

This project builds a standalone RAG service, transforming the static `gemini-cli` documentation into a dynamic and queryable tool. This tool exposes

Local serverstdioPython

What is the Gemini Cli Rag MCP Server?

Gemini Cli Rag MCP lets Claude, Cursor and other MCP clients work with Gemini Cli Rag MCP directly. This project builds a standalone RAG service, transforming the static gemini-cli documentation into a dynamic and queryable tool. This tool exposes knowledge via a protocol (like MCP), making it accessible to any integrated client. Therefore, environments like gemini-cli, VS Code, or Cursor can.

What Gemini Cli Rag MCP does

This project builds a standalone RAG service, transforming the static gemini-cli documentation into a dynamic and queryable tool. This tool exposes knowledge via a protocol (like MCP), making it accessible to any integrated client. Therefore, environments like gemini-cli, VS Code, or Cursor can provide developers with instant, accurate answers in natural language, directly within their workflow. Accelerating learning and letting you intuitively leverage the tool's full potential.

Key capabilities

  • RAG-based Q&A — Ask questions about the Gemini CLI in natural language and get answers based on its official documentation
  • Local Vector Store — The entire documentation is stored and indexed locally using SKLearnVectorStore
  • Extensible — The MCP server can be easily extended with new tools and data sources

Requirements

  • Python 3.13 - Node.js 18+ - An existing gemini-cli installation. If you don't have it, you can clone the official repository:

Practical notes

  • Maintained by pedarias, written in Python.

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

Maintainer setup documentation.

How to install the Gemini Cli Rag MCP Server

{
  "mcpServers": {
    "gemini_docs": {
      "command": "/absolute/path/to/python-environment/bin/python",
      "args": [
        "/absolute/path/to/gemini-cli-rag-mcp/gemini_cli_mcp.py"
      ],
      "env": {
        "RAG_MODEL": "e5-small",
        "RAG_INDEX_DIR": "/absolute/path/to/gemini-cli-rag-mcp/indexes/e5-small-v0.60.0"
      }
    }
  }
}

From the project documentation. Replace placeholders and example paths before connecting.

Configuration

  • Python 3.13 - Node.js 18+ - An existing gemini-cli installation. If you don't have it, you can clone the official repository:
VariableDescriptionRequired
RAG_MODELDeclared in the maintainer’s client configuration.Check documentation
RAG_INDEX_DIRDeclared in the maintainer’s client configuration.Check documentation

Example prompts to try

  • “Use Gemini Cli Rag MCP to Prerequisites.”
  • “Use Gemini Cli Rag MCP to Installation.”

Frequently asked questions

This project builds a standalone RAG service, transforming the static `gemini-cli` documentation into a dynamic and queryable tool. This tool exposes knowledge via Confirm the tools offered by your installed version in the connected client. Tool availability and permissions depend on the server and account configuration.

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