Gemini MCP Server

An MCP (Model Context Protocol) server that provides access to Google Gemini AI models.

Quick Start

Install dependencies:

npm install

Create a .env file with your Gemini API key:

GEMINI_API_KEY=your_api_key_here

Start the server:

npm run dev

The server will run at http://localhost:3333/mcp

Available Tool

`gemini.generateText`

Generate text using Google Gemini models.

Parameters:

prompt (string, required): The text prompt
model (string, optional): Gemini model to use (default: gemini-2.5-pro)
temperature (number, optional): Temperature for generation, 0-2 (default: 1)

Returns:

text: Generated text response
model: Model used
temperature: Temperature setting used

Usage Example

import { Client as McpClient } from '@modelcontextprotocol/sdk/client/index.js'; import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp.js'; const transport = new StreamableHTTPClientTransport( new URL('http://localhost:3333/mcp') ); const client = new McpClient( { name: 'my-client', version: '1.0.0' }, { capabilities: {} } ); await client.connect(transport); const result = await client.callTool({ name: 'gemini.generateText', arguments: { prompt: 'Explain AI in simple terms', model: 'gemini-2.5-pro', temperature: 0.7 } }); console.log(result); await client.close();

Testing

Run the included test client (requires server to be running):

npm test

Configuration

Environment variables:

GEMINI_API_KEY (required): Your Google Gemini API key
PORT (optional): Server port (default: 3333)

Deployment

Deploy to Google Cloud Run with automated scripts:

# 0. Prerequisites (one-time, via GCP Console) # - Create GCP project # - Link billing account # - Run: gcloud auth login # 1. Setup GCP (one-time) ./bin/setup-gcp.sh \ --project-id=my-project \ --github-user=your-username \ --github-repo=gemini-mcp # 2. Set GitHub Secrets (follow script output) # 3. Deploy git push origin main

For detailed deployment instructions, see DEPLOYMENT.md.