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Gemini CLI

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jamubc

MCP server that enables AI assistants to interact with Google Gemini CLI, leveraging Gemini's massive token window for large file analysis and codebase understanding

Publisherjamubc
Repositorygemini-mcp-tool
LanguageTypeScript
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193
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2.2K
Available tools
6
Transport typestdio
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  • Connect tools to AI workflows

    Gemini CLI exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 6 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    2.2K stars and 193 forks from the linked repository.

Gemini MCP Tool

GitHub Release npm version npm downloads License: MIT Open Source

📚 View Full Documentation - Search me!, Examples, FAQ, Troubleshooting, Best Practices

This is a simple Model Context Protocol (MCP) server that allows AI assistants to interact with the Gemini CLI. It enables the AI to leverage the power of Gemini's massive token window for large analysis, especially with large files and codebases using the @ syntax for direction.

  • Ask gemini natural questions, through claude or Brainstorm new ideas in a party of 3!

TLDR: Claude + Google Gemini

Goal: Use Gemini's powerful analysis capabilities directly in Claude Code to save tokens and analyze large files.

Prerequisites

Before using this tool, ensure you have:

  1. Node.js (v16.0.0 or higher)
  2. Google Gemini CLI installed and configured

One-Line Setup

bash
claude mcp add gemini-cli -- npx -y gemini-mcp-tool

Verify Installation

Type /mcp inside Claude Code to verify the gemini-cli MCP is active.


Alternative: Import from Claude Desktop

If you already have it configured in Claude Desktop:

  1. Add to your Claude Desktop config:
json
"gemini-cli": {
  "command": "npx",
  "args": ["-y", "gemini-mcp-tool"]
}
  1. Import to Claude Code:
bash
claude mcp add-from-claude-desktop

Configuration

Register the MCP server with your MCP client:

For NPX Usage (Recommended)

Add this configuration to your Claude Desktop config file:

json
{
  "mcpServers": {
    "gemini-cli": {
      "command": "npx",
      "args": ["-y", "gemini-mcp-tool"]
    }
  }
}

For Global Installation

If you installed globally, use this configuration instead:

json
{
  "mcpServers": {
    "gemini-cli": {
      "command": "gemini-mcp"
    }
  }
}

Configuration File Locations:

  • Claude Desktop:
    • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
    • Windows: %APPDATA%\Claude\claude_desktop_config.json
    • Linux: ~/.config/claude/claude_desktop_config.json

After updating the configuration, restart your terminal session.

Example Workflow

  • Natural language: "use gemini to explain index.html", "understand the massive project using gemini", "ask gemini to search for latest news"
  • Claude Code: Type /gemini-cli and commands will populate in Claude Code's interface.

Usage Examples

With File References (using @ syntax)

  • ask gemini to analyze @src/main.js and explain what it does
  • use gemini to summarize @. the current directory
  • analyze @package.json and tell me about dependencies

General Questions (without files)

  • ask gemini to search for the latest tech news
  • use gemini to explain div centering
  • ask gemini about best practices for React development related to @file_im_confused_about

Using Gemini CLI's Sandbox Mode (-s)

The sandbox mode allows you to safely test code changes, run scripts, or execute potentially risky operations in an isolated environment.

  • use gemini sandbox to create and run a Python script that processes data
  • ask gemini to safely test @script.py and explain what it does
  • use gemini sandbox to install numpy and create a data visualization
  • test this code safely: Create a script that makes HTTP requests to an API

Tools (for the AI)

These tools are designed to be used by the AI assistant.

  • ask-gemini: Asks Google Gemini for its perspective. Can be used for general questions or complex analysis of files.
    • prompt (required): The analysis request. Use the @ syntax to include file or directory references (e.g., @src/main.js explain this code) or ask general questions (e.g., Please use a web search to find the latest news stories).
    • model (optional): The Gemini model to use. Defaults to gemini-2.5-pro.
    • sandbox (optional): Set to true to run in sandbox mode for safe code execution.
  • sandbox-test: Safely executes code or commands in Gemini's sandbox environment. Always runs in sandbox mode.
    • prompt (required): Code testing request (e.g., Create and run a Python script that... or @script.py Run this safely).
    • model (optional): The Gemini model to use.
  • Ping: A simple test tool that echoes back a message.
  • Help: Shows the Gemini CLI help text.

Slash Commands (for the User)

You can use these commands directly in Claude Code's interface (compatibility with other clients has not been tested).

  • /analyze: Analyzes files or directories using Gemini, or asks general questions.
    • prompt (required): The analysis prompt. Use @ syntax to include files (e.g., /analyze prompt:@src/ summarize this directory) or ask general questions (e.g., /analyze prompt:Please use a web search to find the latest news stories).
  • /sandbox: Safely tests code or scripts in Gemini's sandbox environment.
    • prompt (required): Code testing request (e.g., /sandbox prompt:Create and run a Python script that processes CSV data or /sandbox prompt:@script.py Test this script safely).
  • /help: Displays the Gemini CLI help information.
  • /ping: Tests the connection to the server.
    • message (optional): A message to echo back.

Contributing

Contributions are welcome! Please see our Contributing Guidelines for details on how to submit pull requests, report issues, and contribute to the project.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Disclaimer: This is an unofficial, third-party tool and is not affiliated with, endorsed, or sponsored by Google.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "gemini-cli": {
      "command": "npx",
      "args": [
        "-y",
        "gemini-mcp-tool"
      ]
    }
  }
}

Available Tools

  • ask-gemini

    model selection [-m], sandbox [-s], and changeMode:boolean for providing edits

  • ping

    Echo

  • Help

    receive help information

  • brainstorm

    Generate novel ideas with dynamic context gathering. --> Creative frameworks (SCAMPER, Design Thinking, etc.), domain context integration, idea clustering, feasibility analysis, and iterative refinement.

  • fetch-chunk

    Retrieves cached chunks from a changeMode response. Use this to get subsequent chunks after receiving a partial changeMode response.

  • timeout-test

    Test timeout prevention by running for a specified duration

Use Gemini CLI MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Gemini CLI is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Gemini CLI as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Gemini CLI MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "gemini-cli": {
      "command": "npx",
      "args": [
        "-y",
        "gemini-mcp-tool"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Gemini CLI MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Gemini CLI MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Gemini CLI to help me with this task?
Gemini CLI
Sure. I read it.
Here is what I found using Gemini CLI.

Frequently asked questions

What is the Gemini CLI MCP server used for?

Gemini CLI is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Gemini CLI MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Gemini CLI with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Gemini CLI MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Gemini CLI connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Gemini CLI MCP to TypingMind?

Gemini CLI runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Gemini CLI MCP provide in TypingMind?

Gemini CLI exposes 6 MCP tools that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Gemini CLI MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Gemini CLI requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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