WanMCP
WanMCP is an MCP server for generating and tracking Wan AI videos through AceDataCloud.
Generate text-to-video from prompts with options for model, resolution (480P/720P/1080P), aspect ratio, duration, audio, seed, watermark, negative prompt, prompt extension, and callback URL.
Generate image-to-video using a reference image, including standard, fast, and reference-video models, plus character/timbre extraction and single or multi-shot editing.
Generate all-in-one Wan 3 videos from text, first/last frames, reference images/videos/audio, files, or public links with ordered media and automatic duration.
Query one task or batches of tasks for status, results, video URLs, and metadata; task states include pending, processing, completed, and failed.
List available models, resolutions, and API actions for reference.
Provides integration with Amazon Q Developer, allowing users to generate AI videos from text and images using Wan video generation models by configuring the MCP server endpoint within the Amazon Q Developer environment.
Enables AI video generation capabilities within JetBrains IDEs by connecting to the Wan MCP server through the Model Context Protocol integration in the AI Assistant, supporting text-to-video and image-to-video generation workflows.
WanMCP
A Model Context Protocol (MCP) server for AI video generation using Wan through the AceDataCloud API.
Generate AI videos from text or images directly from Claude, VS Code, or any MCP-compatible client.
Features
Text to Video - Create AI-generated videos from text prompts
Image to Video - Generate videos using reference images
Multiple Models - Support for 5 Wan models (wan2.6-t2v, wan2.6-i2v, wan2.6-r2v, wan2.6-i2v-flash, wan3.0-video)
Multiple Resolutions - 480P (draft), 720P (default), 1080P (high quality)
Audio Support - Generate videos with sound
Character Transfer - Extract character appearance via reference videos (wan2.6-r2v)
Task Tracking - Monitor generation progress and retrieve results
Related MCP server: HailuoMCP
Tool Reference
Tool | Description |
| Generate AI video from a text prompt using Wan. |
| Generate AI video using a reference image as the starting frame. |
| Query the status and result of a video generation task. |
| Query multiple video generation tasks at once. |
| List all available Wan models for video generation. |
| List all available resolution options. |
| List all available Wan API actions and corresponding tools. |
Connect: hosted OAuth, API token, or local stdio
The hosted endpoint is https://wan.mcp.acedata.cloud/mcp. Choose one route for the MCP client:
Route | When to use it | Credential setup |
Hosted OAuth | The client supports remote MCP OAuth | Add only the URL, then sign in to AceDataCloud and approve access. No token needs to be pasted into client configuration. |
Hosted API token | The client cannot finish OAuth, or you need an explicit integration credential | Send an AceDataCloud API token in the |
Local stdio | The client runs a local MCP process | Install |
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, enterhttps://wan.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 localclaude_desktop_config.jsonis a separate setup. Claude connector guide.Claude Code:
claude mcp add --transport http --scope user wan https://wan.mcp.acedata.cloud/mcp, thenclaude mcp login wan. Check/mcp. Claude Code MCP guide.Cursor: Add a remote server with only
https://wan.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://wan.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.jsonor the user profile. Check MCP: List Servers. VS Code MCP setup.Codex:
codex mcp add wan --url https://wan.mcp.acedata.cloud/mcp, thencodex mcp login wan. Its user settings are in~/.codex/config.toml. Official Codex MCP guide.
Cursor project config (OAuth):
{
"mcpServers": {
"wan": {"url": "https://wan.mcp.acedata.cloud/mcp"}
}
}VS Code-specific workspace config (OAuth):
{
"servers": {
"wan": {"type": "http", "url": "https://wan.mcp.acedata.cloud/mcp"}
}
}Hosted API token
Open Ace Data Cloud → Applications. Sign in, or register an account first.
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.
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.
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.
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 HTTPAuthorizationheader needs theBearerprefix. Aplatform-...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 wan https://wan.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": {
"wan": {
"type": "http",
"url": "https://wan.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": {
"wan": {
"url": "https://wan.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-wan-token", "type": "promptString", "description": "AceDataCloud API token", "password": true}
],
"servers": {
"wan": {
"type": "http",
"url": "https://wan.mcp.acedata.cloud/mcp",
"headers": {"Authorization": "Bearer ${input:acedata-wan-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-wan
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
mcp-wanFor 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": {
"wan": {
"command": "uvx",
"args": ["mcp-wan"],
"env": {"ACEDATACLOUD_API_TOKEN": "YOUR_API_TOKEN"}
}
}
}Keep this user-level file private. Self-hosted HTTP uses mcp-wan --transport http --port 8000; expose it only with suitable network and TLS controls. Local execution still calls the AceDataCloud API.
Check before using the service
https://wan.mcp.acedata.cloud/healthreturning{"status":"ok"}checks endpoint reachability only.Confirm that the MCP client loads tools.
wan_list_modelsis a reference tool; it does not verify downstream API access or balance.If you need a full API check, call
wan_generate_videowith your own valid input after reviewing current service documentation and displayed pricing. If the result contains a task ID, callwan_get_taskon 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.
Available Models
Model | Description | Use Case |
| Text to video | Generate video from text prompts |
| Image to video | Standard image-to-video generation |
| Reference video-to-video | Character extraction and transfer |
| Fast image to video | Quick preview, lower quality |
| Text to video | Video generation with optional media |
Configuration
Environment Variables
Variable | Description | Default |
| API token from AceDataCloud | Required |
| API base URL |
|
| Default video model |
|
| Default resolution |
|
| Request timeout in seconds |
|
| Logging level |
|
Command Line Options
mcp-wan --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/WanMCP.git
cd WanMCP
# 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
WanMCP/
├── core/ # Core modules
│ ├── __init__.py
│ ├── client.py # HTTP client for Wan API
│ ├── config.py # Configuration management
│ ├── exceptions.py # Custom exceptions
│ ├── oauth.py # OAuth 2.1 provider
│ ├── 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
│ └── __init__.py
├── deploy/ # Deployment configs
│ └── production/
│ ├── deployment.yaml
│ ├── ingress.yaml
│ └── service.yaml
├── .env.example # Environment template
├── 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 Wan API:
Wan Videos API - Video generation (text2video, image2video)
Wan 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
Documentation
License
MIT License - see LICENSE for details.
Links
Made with love by AceDataCloud
API parameter behavior
wan_generate_video accepts shot_type="single" or "multi" for supported text-to-video models, matching the image tool and public API.
Available Tools
8 toolswan_generate_videoAInspect
Generate AI video from a text prompt using Wan text-to-video model.
This uses the wan2.6-t2v model to create video from text descriptions.
For creating video from images, use wan_generate_video_from_image instead.
Returns:
Task ID and generated video information including URLs and state.
Inputs: prompt (required), seed, size, audio, +10 more. Returns: result
wan_generate_video_all_in_oneCInspect
Generate a Wan 3 video from text, frames, reference media, a file, or a public link.
Inputs: seed, audio, media, ratio, +5 more. Returns: result
wan_generate_video_from_imageCInspect
Generate AI video from a reference image using Wan image-to-video models.
This supports three models:
- wan2.6-i2v: Standard image-to-video generation
- wan2.6-r2v: Reference video-to-video with character/timbre extraction
- wan2.6-i2v-flash: Fast image-to-video generation
Returns:
Task ID and generated video information including URLs and state.
Inputs: prompt (required), image_url (required), seed, size, +13 more. Returns: result
wan_get_taskAInspect
Query the status and result of a video generation task.
Use this to check if a generation is complete and retrieve the resulting
video URLs and metadata.
Task states:
- 'pending': Generation is queued
- 'processing': Generation is in progress
- 'completed': Generation finished successfully
- 'failed': Generation failed (check error message)
Returns:
Task status and generated video information including URLs.
Inputs: task_id (required). Returns: result
wan_get_tasks_batchAInspect
Query multiple video generation tasks at once.
Efficiently check the status of multiple tasks in a single request.
Returns:
Status and video information for all queried tasks.
Inputs: task_ids (required). Returns: result
wan_list_actionsAInspect
List all available Wan API actions and corresponding tools.
Reference guide for what each action does and which tool to use.
Returns:
Categorized list of all actions and their corresponding tools.
Inputs: no named parameters. Returns: result
wan_list_modelsAInspect
List all available Wan video generation models.
Shows models with their capabilities, supported actions, and use cases.
Returns:
Table of all models with descriptions and constraints.
Inputs: no named parameters. Returns: result
wan_list_resolutionsAInspect
List all available resolutions for Wan video generation.
Shows resolution options with their quality and use cases.
Returns:
Table of resolutions with descriptions.
Inputs: no named parameters. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 8 tools
The list_* and get_task tools are clearly distinct, but wan_generate_video, wan_generate_video_from_image, and especially wan_generate_video_all_in_one overlap heavily since all_in_one accepts text, frames, reference media, files, and links. An agent has no clear guidance on when to prefer the specialized tools over the all-in-one.
All tools use a consistent wan_ prefix followed by a verb_noun pattern (wan_generate_video, wan_get_task, wan_list_models), with snake_case throughout. Suffixes like _from_image, _batch, and _all_in_one remain readable and predictable.
Eight tools is a well-scoped set for a video generation API covering generation, task polling, and reference listings. The all-in-one tool is arguably redundant with the specialized generators, slightly inflating the count.
The surface covers generation (t2v, i2v, all-in-one), single and batch task polling, and reference listings for actions, models, and resolutions. Minor gaps exist around task lifecycle management (cancel/delete) and direct video download, but core workflows are covered.
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