README.md•1.73 kB
# Gemini MCP Server
An MCP (Model Context Protocol) server that provides access to Google Gemini AI models.
## Quick Start
1. Install dependencies:
```bash
npm install
```
2. Create a `.env` file with your Gemini API key:
```env
GEMINI_API_KEY=your_api_key_here
```
3. Start the server:
```bash
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
```typescript
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):
```bash
npm test
```
## Configuration
Environment variables:
- `GEMINI_API_KEY` (required): Your Google Gemini API key
- `PORT` (optional): Server port (default: 3333)
## License
ISC