mcp-veo 2026.3.19.3

MCP Server for Veo AI Video Generation via AceDataCloud API

MCP Veo

A Model Context Protocol (MCP) server for AI video generation using Veo through the AceDataCloud API.

Generate AI videos from text prompts or images directly from Claude, VS Code, or any MCP-compatible client.

Features

• Text to Video - Create AI-generated videos from text descriptions
• Image to Video - Animate images or create transitions between images
• Multi-Image Fusion - Blend elements from multiple images
• 1080p Upscaling - Get high-resolution versions of generated videos
• Task Tracking - Monitor generation progress and retrieve results
• Multiple Models - Choose between quality and speed with various Veo models

Quick Start

1. Get API Token

Get your API token from AceDataCloud Platform:

1. Sign up or log in
2. Navigate to Veo Videos API
3. Click "Acquire" to get your token

2. Install

# Clone the repository
git clone https://github.com/AceDataCloud/MCPVeo.git
cd MCPVeo

# Install with pip
pip install -e .

# Or with uv (recommended)
uv pip install -e .

3. Configure

# Copy example environment file
cp .env.example .env

# Edit with your API token
echo "ACEDATACLOUD_API_TOKEN=your_token_here" > .env

4. Run

# Run the server
mcp-veo

# Or with Python directly
python main.py

Claude Desktop Integration

Add to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "veo": {
      "command": "mcp-veo",
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

Or if using uv:

{
  "mcpServers": {
    "veo": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/MCPVeo", "mcp-veo"],
      "env": {
        "ACEDATACLOUD_API_TOKEN": "your_api_token_here"
      }
    }
  }
}

Remote HTTP Mode (Hosted)

AceDataCloud hosts a managed MCP server that you can connect to directly — no local installation required.

Endpoint: https://veo.mcp.acedata.cloud/mcp

All requests require a Bearer token in the Authorization header. Get your token from AceDataCloud Platform.

Claude Desktop (Remote)

{
  "mcpServers": {
    "veo": {
      "type": "streamable-http",
      "url": "https://veo.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer your_api_token_here"
      }
    }
  }
}

Cursor / VS Code

In your MCP client settings, add:

• Type: streamable-http
• URL: https://veo.mcp.acedata.cloud/mcp
• Headers: Authorization: Bearer your_api_token_here

JetBrains IDEs

Install the Veo MCP plugin from the JetBrains Marketplace, or configure manually:

1. Go to Settings → Tools → AI Assistant → Model Context Protocol (MCP)
2. Click Add and select HTTP
3. Paste this configuration:

{
  "mcpServers": {
    "veo": {
      "url": "https://veo.mcp.acedata.cloud/mcp",
      "headers": {
        "Authorization": "Bearer your_api_token_here"
      }
    }
  }
}

cURL Test

# Health check (no auth required)
curl https://veo.mcp.acedata.cloud/health

# MCP initialize (requires Bearer token)
curl -X POST https://veo.mcp.acedata.cloud/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer your_api_token_here" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"1.0"}}}'

Self-Hosting with Docker

docker pull ghcr.io/acedatacloud/mcp-veo:latest
docker run -p 8000:8000 ghcr.io/acedatacloud/mcp-veo:latest

Clients connect with their own Bearer token — the server extracts the token from each request's Authorization header and uses it for upstream API calls.

Available Tools

Video Generation

Tool	Description
veo_text_to_video	Generate video from a text prompt
veo_image_to_video	Generate video from reference image(s)
veo_get_1080p	Get high-resolution 1080p version

Tasks

Tool	Description
veo_get_task	Query a single task status
veo_get_tasks_batch	Query multiple tasks at once

Information

Tool	Description
veo_list_models	List available Veo models
veo_list_actions	List available API actions
veo_get_prompt_guide	Get video prompt writing guide

Usage Examples

Generate Video from Text

User: Create a video of a sunset over the ocean

Claude: I'll generate a sunset video for you.
[Calls veo_text_to_video with prompt="Cinematic shot of a golden sunset over the ocean, waves gently rolling, warm colors reflecting on the water"]

Animate an Image

User: Animate this product image to make it rotate slowly

Claude: I'll create a video from your image.
[Calls veo_image_to_video with image_urls=["product_image.jpg"], prompt="Product slowly rotates 360 degrees, studio lighting"]

Create Image Transition

User: Create a video that transitions between these two landscape photos

Claude: I'll create a transition video between your images.
[Calls veo_image_to_video with image_urls=["img1.jpg", "img2.jpg"], prompt="Smooth cinematic transition between scenes"]

Available Models

Model	Text2Video	Image2Video	Image Input
veo2	✅	✅	1 image (first frame)
veo2-fast	✅	✅	1 image (first frame)
veo3	✅	✅	1-3 images
veo3-fast	✅	✅	1-3 images
veo31	✅	✅	1-3 images
veo31-fast	✅	✅	1-3 images
veo31-fast-ingredients	❌	✅	1-3 images (fusion)

Aspect Ratios:

• 16:9 - Landscape/widescreen (default)
• 9:16 - Portrait/vertical (social media)
• 4:3 - Standard
• 3:4 - Portrait standard
• 1:1 - Square

Configuration

Environment Variables

Variable	Description	Default
ACEDATACLOUD_API_TOKEN	API token from AceDataCloud	Required
ACEDATACLOUD_API_BASE_URL	API base URL	https://api.acedata.cloud
VEO_DEFAULT_MODEL	Default model for generation	veo2
VEO_REQUEST_TIMEOUT	Request timeout in seconds	180
LOG_LEVEL	Logging level	INFO

Command Line Options

mcp-veo --help

Options:
  --version          Show version
  --transport        Transport mode: stdio (default) or http
  --port             Port for HTTP transport (default: 8000)

Development

Setup Development Environment

# Clone repository
git clone https://github.com/AceDataCloud/MCPVeo.git
cd MCPVeo

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows

# Install with dev dependencies
pip install -e ".[dev,test]"

Run Tests

# Run unit tests
pytest

# Run with coverage
pytest --cov=core --cov=tools

# Run integration tests (requires API token)
pytest tests/test_integration.py -m integration

Code Quality

# Format code
ruff format .

# Lint code
ruff check .

# Type check
mypy core tools

Build & Publish

# Install build dependencies
pip install -e ".[release]"

# Build package
python -m build

# Upload to PyPI
twine upload dist/*

Project Structure

MCPVeo/
├── core/                   # Core modules
│   ├── __init__.py
│   ├── client.py          # HTTP client for Veo API
│   ├── config.py          # Configuration management
│   ├── exceptions.py      # Custom exceptions
│   ├── server.py          # MCP server initialization
│   ├── types.py           # Type definitions
│   └── utils.py           # Utility functions
├── tools/                  # MCP tool definitions
│   ├── __init__.py
│   ├── video_tools.py     # Video generation tools
│   ├── info_tools.py      # Information tools
│   └── task_tools.py      # Task query tools
├── prompts/                # MCP prompts
│   └── __init__.py
├── tests/                  # Test suite
│   ├── conftest.py
│   ├── test_client.py
│   ├── test_config.py
│   ├── test_integration.py
│   └── test_utils.py
├── deploy/                 # Deployment configs
│   └── production/
│       ├── deployment.yaml
│       ├── ingress.yaml
│       └── service.yaml
├── .env.example           # Environment template
├── .gitignore
├── Dockerfile             # Docker image for HTTP mode
├── docker-compose.yaml    # Docker Compose config
├── LICENSE
├── main.py                # Entry point
├── pyproject.toml         # Project configuration
└── README.md

API Reference

This server wraps the AceDataCloud Veo API:

• Veo Videos API - Video generation
• Veo Tasks API - Task queries

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing)
5. Open a Pull Request

License

MIT License - see LICENSE for details.

Links

• AceDataCloud Platform
• Google Veo
• Model Context Protocol
• MCP Python SDK

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Made with love by AceDataCloud