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WanMCP

PyPI version PyPI downloads Python 3.10+ License: MIT MCP

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

wan_generate_video

Generate AI video from a text prompt using Wan.

wan_generate_video_from_image

Generate AI video using a reference image as the starting frame.

wan_get_task

Query the status and result of a video generation task.

wan_get_tasks_batch

Query multiple video generation tasks at once.

wan_list_models

List all available Wan models for video generation.

wan_list_resolutions

List all available resolution options.

wan_list_actions

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 Authorization: Bearer … header. Keep it in a local secret store or environment variable.

Local stdio

The client runs a local MCP process

Install mcp-wan and pass ACEDATACLOUD_API_TOKEN to that process. It still calls the AceDataCloud API.

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://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 local claude_desktop_config.json is a separate setup. Claude connector guide.

  • Claude Code: claude mcp add --transport http --scope user wan https://wan.mcp.acedata.cloud/mcp, then claude 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.json or the user profile. Check MCP: List Servers. VS Code MCP setup.

  • Codex: codex mcp add wan --url https://wan.mcp.acedata.cloud/mcp, then codex 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

  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.

Copy an application API key or open Manage Keys

Create a separately named key with optional restrictions

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-wan

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": {
    "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

  1. https://wan.mcp.acedata.cloud/health returning {"status":"ok"} checks endpoint reachability only.

  2. Confirm that the MCP client loads tools. wan_list_models is a reference tool; it does not verify downstream API access or balance.

  3. If you need a full API check, call wan_generate_video with your own valid input after reviewing current service documentation and displayed pricing. If the result contains a task ID, call wan_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.

Available Models

Model

Description

Use Case

wan2.6-t2v

Text to video

Generate video from text prompts

wan2.6-i2v

Image to video

Standard image-to-video generation

wan2.6-r2v

Reference video-to-video

Character extraction and transfer

wan2.6-i2v-flash

Fast image to video

Quick preview, lower quality

wan3.0-video

Text to video

Video generation with optional media

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

WAN_DEFAULT_MODEL

Default video model

wan2.6-t2v

WAN_DEFAULT_RESOLUTION

Default resolution

720P

WAN_REQUEST_TIMEOUT

Request timeout in seconds

1800

LOG_LEVEL

Logging level

INFO

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 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

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.md

API 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:

  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.


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 tools
wan_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

    A3.6/5.0

    Scored across 8 tools

    Disambiguation3/5

    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.

    Naming Consistency5/5

    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.

    Tool Count4/5

    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.

    Completeness4/5

    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.

    Maintenance

    ActivityActive
    ResponsivenessOutdated data

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