SeedanceMCP

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.

Connect: hosted OAuth, API token, or local stdio

The hosted endpoint is https://seedance.mcp.acedata.cloud/mcp. Choose one route for the MCP client:

The hosted service advertises OAuth metadata and Dynamic Client Registration (DCR). DCR registers the client application; it is not an API key. OAuth signs you in and the client sends the resulting Bearer token; it may reuse or create an API credential for the account. Browser sign-in still requires an AceDataCloud account. The hosted service can be metered: review current service documentation and displayed pricing before a real operation. Do not configure both an OAuth login and a fixed Authorization header for the same server.

Hosted OAuth examples

• Claude and Claude Desktop chat: Add a remote custom connector in Customize → Connectors → Add custom connector, enter https://seedance.mcp.acedata.cloud/mcp, select sign-in, and choose Register automatically if Claude asks how to register its OAuth client. Complete consent. Claude Desktop's local claude_desktop_config.json is a separate setup. Claude connector guide.
• Claude Code: claude mcp add --transport http --scope user seedance https://seedance.mcp.acedata.cloud/mcp, then claude mcp login seedance. Check /mcp. Claude Code MCP guide.
• Cursor: Add a remote server with only https://seedance.mcp.acedata.cloud/mcp. For a project, merge the entry below into <project>/.cursor/mcp.json; for personal use, use ~/.cursor/mcp.json. Cursor MCP guide.
• VS Code / Copilot: Run MCP: Add Server, select HTTP, enter https://seedance.mcp.acedata.cloud/mcp, then finish the browser sign-in. New portable workspace configs use <project>/.mcp.json; the VS Code-specific format below uses <project>/.vscode/mcp.json or the user profile. Check MCP: List Servers. VS Code MCP setup.
• Codex: codex mcp add seedance --url https://seedance.mcp.acedata.cloud/mcp, then codex mcp login seedance. Its user settings are in ~/.codex/config.toml. Official Codex MCP guide.

Cursor project config (OAuth):

{
  "mcpServers": {
    "seedance": {"url": "https://seedance.mcp.acedata.cloud/mcp"}
  }
}

VS Code-specific workspace config (OAuth):

{
  "servers": {
    "seedance": {"type": "http", "url": "https://seedance.mcp.acedata.cloud/mcp"}
  }
}

Hosted API token

1. Open Ace Data Cloud → Applications. Sign in, or register an account first.
2. Find General application. If it has not been created, choose Initialize General Application. A general key can use the services available to your account; a dedicated application's key is limited to its service. Check the required service/model access and current pricing.
3. Copy the API Key from the application card, or open Keys / Manage Keys → Create to create a separate key for this integration. Give it a recognizable name so you can revoke or rotate it independently.
4. When creating a key, review Expiration Time, Set Usage Limitation, and Allowed APIs. Enable only the restrictions you need, allow the operations you will call, then click Create and copy the token from the key list. Task lookup does not grant permission to submit a generation. Confirm the application's balance can cover your first request; any trial allowance is whatever your account currently displays.
5. Copy only the token value, without quotation marks, spaces, or Bearer . An API Key / API Token field expects this raw value; only a manually configured HTTP Authorization header needs the Bearer prefix. A platform-... account-management token cannot replace a generation API key.

Save the key in the client's credential store or the environment variable shown below. Never put it in a prompt, shared workflow, screenshot, or Git commit. If authentication fails, check the full copied value, application/service access, expiration, usage limit, and balance before creating another key.

A fixed Bearer header bypasses OAuth. If the header is invalid, remove it before trying browser sign-in; keep the saved credential private.

For Claude Code, the shell expands the token when you add the server; treat the saved user MCP config as a secret:

export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
claude mcp add --transport http --scope user seedance https://seedance.mcp.acedata.cloud/mcp \
  --header "Authorization: Bearer $ACEDATACLOUD_API_TOKEN"

For a Claude Code project config, put a variable reference in <project>/.mcp.json and set that variable in the environment that launches Claude Code:

{
  "mcpServers": {
    "seedance": {
      "type": "http",
      "url": "https://seedance.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${ACEDATACLOUD_API_TOKEN}"}
    }
  }
}

Cursor uses a different environment-variable syntax in ~/.cursor/mcp.json or an uncommitted project config:

{
  "mcpServers": {
    "seedance": {
      "url": "https://seedance.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${env:ACEDATACLOUD_API_TOKEN}"}
    }
  }
}

In VS Code, run MCP: Open User Configuration and merge this server plus its masked input; ${input:...} is for VS Code's user/workspace format and is not portable to the Agent Host .mcp.json format:

{
  "inputs": [
    {"id": "acedata-seedance-token", "type": "promptString", "description": "AceDataCloud API token", "password": true}
  ],
  "servers": {
    "seedance": {
      "type": "http",
      "url": "https://seedance.mcp.acedata.cloud/mcp",
      "headers": {"Authorization": "Bearer ${input:acedata-seedance-token}"}
    }
  }
}

For Cline, use its MCP configuration UI or CLI file ~/.cline/data/settings/cline_mcp_settings.json; its remote transport value is streamableHttp. For JetBrains AI Assistant, add a remote URL from Settings → Tools → AI Assistant → Model Context Protocol (MCP). For Zed, use a context_servers entry with the URL only for OAuth or add a local Bearer header. These clients have different configuration schemas; follow their current UI rather than copying another client's JSON. Cline · JetBrains · Zed.

Local stdio

Install the package and give the local process an API token:

python -m pip install mcp-seedance
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
mcp-seedance

For Claude Desktop local MCP, merge this entry into the file opened by its developer settings (~/Library/Application Support/Claude/claude_desktop_config.json on macOS). uvx requires uv on PATH:

{
  "mcpServers": {
    "seedance": {
      "command": "uvx",
      "args": ["mcp-seedance"],
      "env": {"ACEDATACLOUD_API_TOKEN": "YOUR_API_TOKEN"}
    }
  }
}

Keep this user-level file private. Self-hosted HTTP uses mcp-seedance --transport http --port 8000; expose it only with suitable network and TLS controls. Local execution still calls the AceDataCloud API.

Example prompt

Verify your first result

Generate a short video of clouds moving over a mountain, then retrieve the completed task and final video URL.

This is an example prompt, not a promised runtime or recorded success. Save the task ID, wait for terminal success, and inspect the final media.

Check before using the service

1. https://seedance.mcp.acedata.cloud/health returning {"status":"ok"} checks endpoint reachability only.
2. Confirm that the MCP client loads tools. seedance_list_models is a reference tool; it does not verify downstream API access or balance.
3. If you need a full API check, call seedance_generate_video with your own valid input after reviewing current service documentation and displayed pricing. If the result contains a task ID, call seedance_get_task on that same ID until terminal success or failure. Do not resubmit the operation just to check progress.

For 401, check which auth route the client used and whether the token or OAuth session is valid. A 403 may mean an account permission or content moderation failure; read the returned error. Insufficient balance and downstream service failures need their own diagnosis. A listed tool or submitted task does not prove a successful result.

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 2.0 (incl. Fast/Mini, multimodal reference), 1.5 Pro, 1.0 Pro, 1.0 Pro Fast, 1.0 Lite T2V/I2V
• Multiple Resolutions - 480p, 720p (default), 1080p, and 4k output (2.5 supports 1080p; 4k: doubao-seedance-2-0-260128 only)
• 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 and 2.0 series)
• Service Tiers - Default (priority) and Flex (cost-effective) processing
• Task Tracking - Monitor generation progress and retrieve results

Tool Reference

Available Tools

Video Generation

Tasks

Information

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-251215"]

Available Models

Available Aspect Ratios

Configuration

Environment Variables

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/SeedanceMCP.git
cd SeedanceMCP

# 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

SeedanceMCP/
├── 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.md

API Reference

This server wraps the AceDataCloud Seedance API:

• Seedance Videos API - Video generation
• Seedance 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

Documentation

Documentation

License

MIT License - see LICENSE for details.

Links

• AceDataCloud Platform
• ByteDance Seedance
• Model Context Protocol
• MCP Python SDK

Made with love by AceDataCloud