MCP Google Vertex AI Server

A Model Context Protocol (MCP) server that provides AI-powered image and video generation capabilities using Google Vertex AI's Imagen and Veo models.

Features

• 🎨 Image Generation: Create AI images using Google's Imagen model

• 🎬 Video Generation: Generate AI videos using Google's Veo model

• 💾 Local Storage: Automatically save generated content to local server storage

• 🔒 Secure Configuration: Environment-based configuration for API credentials

• 🚀 Express v5: Built on the latest Express framework

• 📝 TypeScript: Fully typed for better developer experience

• ♻️ DRY Principles: Clean, maintainable, and reusable code architecture

Prerequisites

• Node.js 24.0.0 or higher

• Google Cloud Project with Vertex AI API enabled

• Service account credentials with appropriate permissions

MCP Tools

generate-image

Generate AI images using the configured Imagen model (set via VERTEX_AI_IMAGE_MODEL).

Parameters:

Example:

{
  "name": "generate-image",
  "arguments": {
    "prompt": "A serene mountain landscape at sunset with a lake",
    "aspectRatio": "16:9",
    "numberOfImages": 2
  }
}

generate-video

Generate AI videos using the configured Veo model (set via VERTEX_AI_VIDEO_MODEL).

Parameters:

Reference images (provide either a local file path, Cloud Storage URI, or public URL):

• Local file path: /path/to/image.png

• Cloud Storage URI: gs://my-bucket/image.jpg

• Public URL: https://cdn.example.com/image.jpg

Supported formats: JPEG, PNG. Maximum size: 10 MB.

referenceImages supports up to 3 ASSET images or 1 STYLE image.

Example — text to video:

{
  "name": "generate-video",
  "arguments": {
    "prompt": "A butterfly flying through a garden of flowers",
    "durationSeconds": 8,
    "aspectRatio": "16:9",
    "resolution": "1080p"
  }
}

Example — image reference:

{
  "name": "generate-video",
  "arguments": {
    "prompt": "The product spinning on a white background",
    "referenceImages": [
      {
        "image": "/path/to/product.png",
        "referenceType": "ASSET"
      }
    ]
  }
}

Connecting to MCP Clients

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "google-vertex": {
      "command": "npx",
      "args": ["mcp-remote", "http://localhost:3005/mcp"]
    }
  }
}

VS Code

Add to your .vscode/mcp.json:

{
  "servers": {
    "google-vertex": {
      "type": "http",
      "url": "http://localhost:3005/mcp"
    }
  }
}

MCP Inspector

Test your server with the MCP Inspector:

npx @modelcontextprotocol/inspector

Then connect to: http://localhost:3005/mcp

Architecture

The server follows clean architecture principles with separation of concerns:

• Config Layer: Environment variable management and validation

• Service Layer: Vertex AI integration and storage management

• Tools Layer: Shared utilities (e.g. reference image resolution)

• Server Layer: MCP protocol implementation and Express server setup

Error Handling

The server includes comprehensive error handling:

• Graceful error responses for tool invocations

• Detailed error messages for troubleshooting

• Proper HTTP status codes

Performance Tips

• Use appropriate aspect ratios and resolutions for your use case

• Monitor Vertex AI quotas and billing

• Consider implementing request queuing for high-traffic scenarios

License

MIT

Acknowledgments

• Built with the Model Context Protocol SDK

• Powered by Google Vertex AI

• Uses Express v5