gemini-mcp

🥈Silver

Gemini-MCP enables Claude Code to interact with Google's Gemini models. Operations teams can automate workflows by connecting these AI systems. It integrates with TypeScript-based tools and supports CLI for easy management.

136190Updated Today

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/RLabs-Inc/gemini-mcp.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/RLabs-Inc/gemini-mcp

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I'm setting up a Gemini-MCP server to enable Claude to interact with Gemini. Here's my setup: [COMPANY] is [INDUSTRY]. I have [DATA] that needs processing. Please guide me through the steps to configure the server, including [SPECIFIC_TOOLS] I want to use. Provide troubleshooting tips for common issues.

Example Output

# Gemini-MCP Server Setup Guide

## Step 1: Prerequisites
- Ensure you have Python 3.8 or later installed
- Install the required libraries: `pip install fastapi uvicorn google-generativeai`
- Set up a Google Cloud project and enable the Gemini API

## Step 2: Server Configuration
```python
from fastapi import FastAPI
import google.generativeai as genai

app = FastAPI()

@app.post("/gemini")
async def process_data(data: dict):
    genai.configure(api_key="YOUR_API_KEY")
    model = genai.GenerativeModel("gemini-pro")
    response = model.generate_content(data["prompt"])
    return {"response": response.text}
```

## Step 3: Running the Server
- Save the script as `server.py`
- Run the server: `uvicorn server:app --reload`
- Access the server at `http://127.0.0.1:8000/gemini`

## Troubleshooting
- **Error 403**: Check your API key and project permissions
- **Connection Issues**: Verify your internet connection and firewall settings
- **Rate Limits**: Monitor your API usage and implement retry logic