Enables AI video generation using ByteDance Seedance models, supporting text-to-video and image-to-video creation, synchronized audio generation, and task status tracking.
MCP Seedance
A Model Context Protocol (MCP) server for AI video generation using ByteDance Seedance through the AceDataCloud API.
Generate AI videos directly from Claude, VS Code, or any MCP-compatible client.
Features
Text to Video - Create AI-generated videos from text prompts
Image to Video - Animate images with first frame, last frame, and reference image control
Multiple Models - Support for Seedance 1.5 Pro, 1.0 Pro, 1.0 Pro Fast, 1.0 Lite T2V/I2V
Multiple Resolutions - 480p, 720p (default), and 1080p output
Flexible Aspect Ratios - 16:9, 9:16, 1:1, 4:3, 3:4, 21:9, and adaptive
Audio Generation - Generate synchronized audio for videos (1.5 Pro)
Service Tiers - Default (priority) and Flex (cost-effective) processing
Task Tracking - Monitor generation progress and retrieve results
Quick Start
1. Get API Token
Get your API token from AceDataCloud Platform:
Sign up or log in
Navigate to Seedance Videos API
Click "Acquire" to get your token
2. Install
# Clone the repository
git clone https://github.com/AceDataCloud/MCPSeedance.git
cd MCPSeedance
# 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" > .env4. Run
# Run the server
mcp-seedance
# Or with Python directly
python main.pyClaude 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": {
"seedance": {
"command": "mcp-seedance",
"env": {
"ACEDATACLOUD_API_TOKEN": "your_api_token_here"
}
}
}
}Or if using uv:
{
"mcpServers": {
"seedance": {
"command": "uv",
"args": ["run", "--directory", "/path/to/mcp-seedance", "mcp-seedance"],
"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://seedance.mcp.acedata.cloud/mcp
All requests require a Bearer token in the Authorization header. Get your token from AceDataCloud Platform.
Claude Desktop (Remote)
{
"mcpServers": {
"seedance": {
"type": "streamable-http",
"url": "https://seedance.mcp.acedata.cloud/mcp",
"headers": {
"Authorization": "Bearer your_api_token_here"
}
}
}
}Cursor / VS Code
In your MCP client settings, add:
Type:
streamable-httpURL:
https://seedance.mcp.acedata.cloud/mcpHeaders:
Authorization: Bearer your_api_token_here
cURL Test
# Health check (no auth required)
curl https://seedance.mcp.acedata.cloud/health
# MCP initialize (requires Bearer token)
curl -X POST https://seedance.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-seedance:latest
docker run -p 8000:8000 ghcr.io/acedatacloud/mcp-seedance:latestClients 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 |
| Generate video from a text prompt |
| Generate video using reference/start/end images |
Tasks
Tool | Description |
| Query a single task status |
| Query multiple tasks at once |
Information
Tool | Description |
| List available Seedance models |
| List available output resolutions |
| List available API actions |
Usage Examples
Generate Video from Prompt
User: Create a video of a cat playing with a ball of yarn
Claude: I'll generate a video for you.
[Calls seedance_generate_video with prompt="A cute cat playfully batting a ball of yarn"]Animate an Image
User: Turn this image into a video: https://example.com/landscape.jpg
Claude: I'll create a video from your image.
[Calls seedance_generate_video_from_image with first_frame_url and appropriate prompt]Generate with Audio
User: Create a video of rain falling with sound
Claude: I'll generate a video with synchronized audio.
[Calls seedance_generate_video with prompt="Rain falling on a quiet street" and generate_audio=True, model="doubao-seedance-1-5-pro-250528"]Available Models
Model | Description | Features |
| 1.5 Pro | Audio generation, T2V, I2V |
| 1.0 Pro (default) | High quality T2V, I2V |
| 1.0 Pro Fast | Faster generation |
| 1.0 Lite T2V | Lightweight text-to-video |
| 1.0 Lite I2V | Lightweight image-to-video |
Available Aspect Ratios
Aspect Ratio | Description | Use Case |
| Landscape (default) | YouTube, TV, presentations |
| Portrait | TikTok, Instagram Reels |
| Square | Instagram posts |
| Traditional | Classic video format |
| Portrait traditional | Portrait content |
| Ultrawide | Cinematic content |
| Adaptive | Auto-detect from image |
Configuration
Environment Variables
Variable | Description | Default |
| API token from AceDataCloud | Required |
| API base URL |
|
| Default model |
|
| Default resolution |
|
| Default aspect ratio |
|
| Default duration (seconds) |
|
| Request timeout in seconds |
|
| Logging level |
|
Command Line Options
mcp-seedance --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/MCPSeedance.git
cd MCPSeedance
# 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 integrationCode Quality
# Format code
ruff format .
# Lint code
ruff check .
# Type check
mypy core toolsBuild & Publish
# Install build dependencies
pip install -e ".[release]"
# Build package
python -m build
# Upload to PyPI
twine upload dist/*Project Structure
MCPSeedance/
├── core/ # Core modules
│ ├── __init__.py
│ ├── client.py # HTTP client for Seedance 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
│ ├── task_tools.py # Task query tools
│ └── info_tools.py # Information tools
├── prompts/ # MCP prompts
│ └── __init__.py # Prompt templates
├── 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
├── CHANGELOG.md
├── Dockerfile # Docker image for HTTP mode
├── docker-compose.yaml # Docker Compose config
├── LICENSE
├── main.py # Entry point
├── pyproject.toml # Project configuration
└── README.mdAPI Reference
This server wraps the AceDataCloud Seedance API:
Seedance Videos API - Video generation
Seedance Tasks API - Task queries
Contributing
Contributions are welcome! Please:
Fork the repository
Create a feature branch (
git checkout -b feature/amazing)Commit your changes (
git commit -m 'Add amazing feature')Push to the branch (
git push origin feature/amazing)Open a Pull Request
License
MIT License - see LICENSE for details.
Links
Made with love by AceDataCloud
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