> ## Documentation Index
> Fetch the complete documentation index at: https://developers.manglai.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini Integration

> Connect the Manglai MCP to Google Gemini

## Gemini + Manglai

### Option 1: Cursor with Gemini model

If you use **Cursor** with the Gemini model, configure the Manglai MCP in `.cursor/mcp.json`:

```json theme={null}
{
  "mcpServers": {
    "manglai": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://mcp.manglai.io/sse",
        "--header",
        "Authorization:Bearer ${MANGLAI_TOKEN}"
      ],
      "env": {
        "MANGLAI_TOKEN": "your_token_here"
      }
    }
  }
}
```

Cursor will handle MCP calls regardless of the underlying model.

### Option 2: Gemini API (programmatic)

If you use the Gemini API directly, you can integrate Manglai by building a backend that combines both APIs:

```javascript theme={null}
import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);
const model = genAI.getGenerativeModel({ model: 'gemini-1.5-pro' });

// When the user asks about emissions, call Manglai:
const manglaiResponse = await fetch(
  'https://www.manglai.io/api/v1/emissions/dashboard?companyId=UUID&startDate=2024-01-01&endDate=2024-12-31',
  { headers: { Authorization: `Bearer ${process.env.MANGLAI_TOKEN}` } }
);
const data = await manglaiResponse.json();

const result = await model.generateContent(
  `Manglai emissions data: ${JSON.stringify(data)}. Analyze and summarize the key insights.`
);
```

### Option 3: Vertex AI

In enterprise environments with Vertex AI, you can use the Manglai REST API as an external data source for grounding or function calling.

<Card title="Generate token" icon="key" href="https://www.manglai.io/saas/settings/api-tokens/docs#models">
  Get your Manglai token
</Card>