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MCP Gemini CLI

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choplin

An MCP server for Google's Gemini CLI, allowing AI assistants to leverage Gemini's capabilities.

Publisherchoplin
Repositorymcp-gemini-cli
LanguageTypeScript
Forks
16
Stars
101
Available tools
0
Transport typestdio
Categories
Links
  • Connect tools to AI workflows

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

  • 0 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

    101 stars and 16 forks from the linked repository.

MCP Gemini CLI

A simple MCP server wrapper for Google's Gemini CLI that enables AI assistants to use Gemini's capabilities through the Model Context Protocol.

What it does

This server exposes three tools that interact with Gemini CLI:

  • googleSearch: Asks Gemini to perform a Google search using your query
  • chat: Sends prompts directly to Gemini for general conversations
  • analyzeFile: Analyzes files (images, PDFs, text) using Gemini's multimodal capabilities

Prerequisites

  • Gemini CLI installed and configured (optional with --allow-npx flag)

🚀 Quick Start with Claude Code

1. Add the MCP server

bash
claude mcp add -s project gemini-cli -- npx mcp-gemini-cli --allow-npx

Or configure your MCP client with the settings shown in the Installation Options section below.

2. Try it out

Example prompts:

  • Search: "Search for the latest TypeScript 5.0 features using Google"
  • Chat: "Ask Gemini to explain the difference between async/await and promises in JavaScript"
  • File Analysis: "Ask Gemini to analyze the image at /path/to/screenshot.png"

🔧 Installation Options

Using npx with --allow-npx flag

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

Local Development

  1. Clone and install:
bash
git clone https://github.com/choplin/mcp-gemini-cli
cd mcp-gemini-cli
bun install
  1. Add to Claude Desktop config:
json
{
  "mcpServers": {
    "mcp-gemini-cli": {
      "command": "bun",
      "args": ["run", "/path/to/mcp-gemini-cli/index.ts"]
    }
  }
}

🛠️ Available Tools

1. googleSearch

Performs a Google search using Gemini CLI.

Parameters:

  • query (required): The search query
  • limit (optional): Maximum number of results
  • sandbox (optional): Run in sandbox mode
  • yolo (optional): Skip confirmations
  • model (optional): Gemini model to use (default: "gemini-2.5-pro")

2. chat

Have a conversation with Gemini.

Parameters:

  • prompt (required): The conversation prompt
  • sandbox (optional): Run in sandbox mode
  • yolo (optional): Skip confirmations
  • model (optional): Gemini model to use (default: "gemini-2.5-pro")

3. analyzeFile

Analyze files using Gemini's multimodal capabilities.

Supported file types:

  • Images: PNG, JPG, JPEG, GIF, WEBP, SVG, BMP
  • Text: TXT, MD, TEXT
  • Documents: PDF

Parameters:

  • filePath (required): The absolute path to the file to analyze
  • prompt (optional): Additional instructions for analyzing the file
  • sandbox (optional): Run in sandbox mode
  • yolo (optional): Skip confirmations
  • model (optional): Gemini model to use (default: "gemini-2.5-pro")

💡 Example Prompts

Try these prompts to see mcp-gemini-cli in action:

  • Search: "Search for the latest TypeScript 5.0 features using Google"
  • Chat: "Ask Gemini to explain the difference between async/await and promises in JavaScript"
  • File Analysis: "Ask Gemini to describe what's in this image: /Users/me/Desktop/screenshot.png"

🛠️ Example Usage

googleSearch

typescript
// Simple search
googleSearch({ query: "latest AI news" });

// Search with limit
googleSearch({
  query: "TypeScript best practices",
  limit: 5,
});

chat

typescript
// Simple chat
chat({ prompt: "Explain quantum computing in simple terms" });

// Using a different model
chat({
  prompt: "Write a haiku about programming",
  model: "gemini-2.5-flash",
});

analyzeFile

typescript
// Analyze an image
analyzeFile({ 
  filePath: "/path/to/image.png",
  prompt: "What objects are in this image?"
});

// Analyze a PDF
analyzeFile({
  filePath: "/path/to/document.pdf",
  prompt: "Summarize the key points in this document"
});

// General analysis without specific instructions
analyzeFile({ filePath: "/path/to/file.jpg" });

📝 Development

Note: Development requires Bun runtime.

Run in Development Mode

bash
bun run dev

Run Tests

bash
bun test

Build for Production

bash
# Development build
bun run build

# Production build (minified)
bun run build:prod

Linting & Formatting

bash
# Lint code
bun run lint

# Format code
bun run format

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

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

📋 Changelog

[0.3.1] - 2025-07-03

Fixed

  • Fixed Windows compatibility issue with which command

[0.3.0] - 2025-07-02

Breaking Changes

  • Tool names: geminiChat → chat, geminiAnalyzeFile → analyzeFile
  • Package name: @choplin/mcp-gemini-cli → mcp-gemini-cli

New Features

  • analyzeFile tool for images (PNG/JPG/GIF/etc), PDFs, and text files

[0.2.0] - Previous

  • Initial release with googleSearch and geminiChat tools

🔗 Related Links

Use MCP Gemini CLI MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once MCP 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 MCP 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 MCP 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": {
    "mcp-gemini-cli": {
      "command": "npx",
      "args": [
        "-y",
        "<mcp-server-package>"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the MCP 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 MCP 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 MCP Gemini CLI to help me with this task?
MCP Gemini CLI
Sure. I read it.
Here is what I found using MCP Gemini CLI.

Frequently asked questions

What is the MCP Gemini CLI MCP server used for?

MCP 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 MCP Gemini CLI MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use MCP 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 MCP 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 MCP 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 MCP Gemini CLI MCP to TypingMind?

MCP 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 MCP Gemini CLI MCP provide in TypingMind?

MCP Gemini CLI exposes MCP capabilities 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 MCP 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 MCP 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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