analytics-datadeveloper-tools

Gemini CLI

jamubc

by jamubc

Integrate with Gemini CLI for large-scale file analysis, secure code execution, and advanced context control using Googl

Integrates with Google's Gemini CLI to leverage massive token windows for analyzing large files and codebases, providing general queries, sandbox-mode code execution for safe testing, and structured response handling with behavioral flags for context control.

github stars

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

best for

  • / General purpose MCP workflows

capabilities

  • / ask-gemini
  • / ping
  • / Help
  • / brainstorm
  • / fetch-chunk
  • / timeout-test

what it does

Integrates with Google's Gemini CLI to leverage massive token windows for analyzing large files and codebases, providing general queries, sandbox-mode code execution for safe testing, and structured response handling with behavioral flags for context control.

about

Gemini CLI is a community-built MCP server published by jamubc that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate with Gemini CLI for large-scale file analysis, secure code execution, and advanced context control using Googl It is categorized under analytics data, developer tools. This server exposes 6 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install Gemini CLI in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

NOASSERTION

Gemini CLI is released under the NOASSERTION license.

readme

Gemini MCP Tool

<div align="center">

GitHub Release npm version npm downloads License: MIT Open Source

</div>

📚 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!
<a href="https://glama.ai/mcp/servers/@jamubc/gemini-mcp-tool"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@jamubc/gemini-mcp-tool/badge" alt="Gemini Tool MCP server" /> </a>

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

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:
"gemini-cli": {
  "command": "npx",
  "args": ["-y", "gemini-mcp-tool"]
}
  1. Import to Claude Code:
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:

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

For Global Installation

If you installed globally, use this configuration instead:

{
  "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.

FAQ

What is the Gemini CLI MCP server?
Gemini CLI is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Gemini CLI?
This profile displays 33 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.733 reviews
  • Yusuf Martinez· Dec 4, 2024

    According to our notes, Gemini CLI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Lucas Gill· Nov 23, 2024

    We wired Gemini CLI into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Amina Sharma· Oct 14, 2024

    Gemini CLI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Arjun Yang· Sep 17, 2024

    According to our notes, Gemini CLI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Rahul Santra· Sep 5, 2024

    Gemini CLI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Dev Menon· Sep 5, 2024

    Useful MCP listing: Gemini CLI is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Evelyn Kim· Sep 5, 2024

    Gemini CLI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Pratham Ware· Aug 24, 2024

    According to our notes, Gemini CLI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Lucas Garcia· Aug 24, 2024

    Gemini CLI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Arjun Martin· Aug 8, 2024

    Gemini CLI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

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