Gemini CLI RAG MCP

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.

Table of Contents

• Project Overview
• Features
• System Architecture
• Getting Started
• Prerequisites
• Installation
• Usage
• 1. Run the MCP Service with Docker
• 2. Configure Gemini CLI
• 3. Ask Questions
• How It Works
• Data Extraction and Vectorization
• MCP Server
• Gemini CLI Integration
• Scripts
• Dependencies

Project Overview

This project integrates a RAG pipeline and it consists of three main components:

1. Data Extraction and Processing: Python scripts that extract content from all markdown files in the gemini-cli/docs directory and sub-directories, process it, and create a vector store.

2. MCP Server: A Python-based MCP server that exposes the vector store as a queryable tool.

3. Gemini CLI/VSCode/ClaudeCode/Windsurf/Cursor...etc: The official Gemini CLI, which can connect to the MCP server to answer questions about its documentation.

Features

• 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.

System Architecture

The system is composed of the following parts:

1. **extract.py**: This script walks through the gemini-cli/docs directory, finds all .md files, and concatenates their content into a single gemini_cli_docs.txt file.

2. **create_vectorstore.py**: This script loads the gemini_cli_docs.txt file, splits it into chunks, and creates a gemini_cli_vectorstore.parquet file using HuggingFaceEmbeddings and SKLearnVectorStore.

3. **gemini_cli_mcp.py**: This script runs a FastMCP server that loads the vector store and exposes two endpoints:

• gemini_cli_query_tool(query: str): A tool that takes a user query, retrieves relevant documents from the vector store, and returns them.
• docs://gemini-cli/full: A resource that returns the entire content of the gemini_cli_docs.txt file.

4. **gemini-cli/**: The official Gemini CLI, which can be configured to use the MCP server.

Getting Started

Prerequisites

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

git clone https://github.com/google-gemini/gemini-cli.git

Installation

1. Clone the repository:

git clone https://github.com/your-username/gemini-cli-rag-mcp.git
    cd gemini-cli-rag-mcp

2. Install Python dependencies:

pip install -r requirements.txt

3. Prepare the documentation data:

Run the extract.py script to gather all the markdown documentation into a single file.

python extract.py

4. Create the vector store:

Run the create_vectorstore.py script to create the vector store from the documentation file.

python create_vectorstore.py

Usage

Before running with docker, try running the mcp in dev mode and test:

mcp dev gemini_cli_mcp.py

On `Command field type 'python' and on Arguments` type 'gemini_cli_mcp.py' and press Connect.

1. Run the MCP Service with Docker

The most efficient way to run the MCP server is with Docker Compose. This starts a container in the background and keeps it ready for Gemini CLI to connect to.

docker-compose up -d

The container will keep running, but the Python MCP script itself will only be executed on-demand by Gemini CLI.

2. Configure Gemini CLI

To make Gemini CLI aware of your local MCP server, you need to create a configuration file.

• Inside the .gemini directory add the following content to the settings.json file:

{
      "mcpServers": {
        "local_rag_server": {
          "command": "docker",
          "args": [
            "exec",
            "-i",
            "gemini-cli-mcp-container",
            "python",
            "gemini_cli_mcp.py"
          ]
        }
      }
    }

This configuration tells Gemini CLI how to launch your MCP server using docker exec.

Obs: To use it in VSCode, go to Settings type 'mcp' and click on settings.json. Then put on Agent mode and ask copilot to implement the gemini-cli-mcp server (give the json above as context).

3. Ask Questions

After restarting terminal to changes make effect, simply run gemini from your terminal. It will automatically discover the local_rag_server and use its tools when needed.

Example:

How do I customize my gemini-cli?

or something more specific:

My gemini cli is not showing an interactive prompt when I run it on my build server, it just exits. I have a CI_TOKEN environment variable set. Why is this happening and how can I fix it?

How It Works

Data Extraction and Vectorization

The extract.py script recursively finds all markdown files in the gemini-cli/docs directory. It reads their content and combines it into a single text file, gemini_cli_docs.txt.

The create_vectorstore.py script then takes this text file and:

1. Loads the document.

2. Splits it into smaller, overlapping chunks using RecursiveCharacterTextSplitter.

3. Uses HuggingFaceEmbeddings (with the BAAI/bge-large-en-v1.5 model) to create embeddings for each chunk.

4. Stores these embeddings in a SKLearnVectorStore, which is persisted to gemini_cli_vectorstore.parquet.

MCP Server

The gemini_cli_mcp.py script creates a FastMCP server. This server defines a tool, gemini_cli_query_tool, which can be called by the Gemini CLI or VSCode/Cursor/etc. When this tool is invoked, it:

1. Loads the persisted SKLearnVectorStore.

2. Uses the vector store as a retriever to find the most relevant document chunks for the given query.

3. Returns the content of these chunks to the Gemini CLI.

Gemini CLI Integration

The Gemini CLI is designed to be extensible through MCP servers. The CLI discovers available tools by connecting to servers defined in the mcpServers object in a settings.json file (either in the project's .gemini directory or in the user's home ~/.gemini directory).

Gemini CLI supports three transport mechanisms for communication:

• Stdio Transport: Spawns a subprocess and communicates with it over stdin and stdout. This is the method used in this project, with the command property in settings.json.
• SSE Transport: Connects to a Server-Sent Events (SSE) endpoint, defined with a url property.
• Streamable HTTP Transport: Uses HTTP streaming for communication, configured with an httpUrl property.

By using the docker exec command, we are leveraging the stdio transport to create a direct communication channel with the Python script inside the container.

Scripts

• **extract.py**: Extracts documentation from markdown files.
• **create_vectorstore.py**: Creates the vector store.
• **gemini_cli_mcp.py**: Runs the MCP server.

Dependencies

Python

The main Python dependencies are listed in requirements.txt:

• langchain: For text splitting, vector stores, and embeddings.
• tiktoken: For token counting.
• sentence-transformers: For the embedding model.
• scikit-learn: For the vector store.
• mcp: For the MCP server.
• fastapi: For the MCP server.

Node.js

The project relies on the gemini-cli package and its dependencies. See gemini-cli/package.json for more details.