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el-el-san_vidu-mcp-server
Mirror ofhttps://github.com/el-el-san/vidu-mcp-server
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Vidu MCP Server
A Model Context Protocol (MCP) server for interacting with the Vidu video generation API. This server provides tools for generating videos from images using Vidu's powerful AI models.
Features
- Image to Video Conversion: Generate videos from static images with customizable settings
- Check Generation Status: Monitor the progress of video generation tasks
- Image Upload: Easily upload images to be used with the Vidu API
Prerequisites
- Node.js (v14 or higher)
- A Vidu API key (available from Vidu website)
- TypeScript (for development)
Installation
- Clone this repository:
git clone https://github.com/el-el-san/vidu-mcp-server.git
cd vidu-mcp-server
- Install dependencies:
npm install
- Create a
.env
file based on the.env.template
and add your Vidu API key:
VIDU_API_KEY=your_api_key_here
Usage
- Build the TypeScript code:
npm run build
- Start the server:
npm start
The MCP server will start and be ready to accept connections from MCP clients.
Tools
1. Image to Video
Converts a static image to a video with customizable parameters.
Parameters:
-
image_url
(required): URL of the image to convert to video -
prompt
(optional): Text prompt for video generation (max 1500 chars) -
duration
(optional): Duration of the output video in seconds (4 or 8, default 4) -
model
(optional): Model name for generation ("vidu1.0", "vidu1.5", "vidu2.0", default "vidu2.0") -
resolution
(optional): Resolution of the output video ("360p", "720p", "1080p", default "720p") -
movement_amplitude
(optional): Movement amplitude of objects in the frame ("auto", "small", "medium", "large", default "auto") -
seed
(optional): Random seed for reproducibility
Example request:
{
"image_url": "https://example.com/image.jpg",
"prompt": "A serene lake with mountains in the background",
"duration": 8,
"model": "vidu2.0",
"resolution": "720p",
"movement_amplitude": "medium",
"seed": 12345
}
2. Check Generation Status
Checks the status of a running video generation task.
Parameters:
-
task_id
(required): Task ID returned by the image-to-video tool
Example request:
{
"task_id": "12345abcde"
}
3. Upload Image
Uploads an image to use with the Vidu API.
Parameters:
-
image_path
(required): Local path to the image file -
image_type
(required): Image file type ("png", "webp", "jpeg", "jpg")
Example request:
{
"image_path": "/path/to/your/image.jpg",
"image_type": "jpg"
}
How It Works
The server uses the Model Context Protocol (MCP) to provide a standardized interface for AI tools. When you start the server, it listens for commands through standard input/output channels and responds with results in a structured format.
The server handles all the complexity of interacting with the Vidu API, including:
- Authentication with API keys
- File uploads and format validation
- Asynchronous task management and polling
- Error handling and reporting
Troubleshooting
-
API Key Issues: Make sure your Vidu API key is correctly set in the
.env
file - File Upload Errors: Check that your image files are valid and under 10MB in size
- Connection Problems: Ensure you have internet access and can reach the Vidu API servers
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
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Reviews

user_MR9Dsm0N
I've been using the el-el-san_vidu-mcp-server by MCP-Mirror, and it's an outstanding application server. The performance is top-notch, and it integrates seamlessly with my projects. The community support and documentation available on the GitHub page make it incredibly easy to set up and use. Highly recommend it to anyone looking for a reliable and efficient server solution! Check it out [here](https://github.com/MCP-Mirror/el-el-san_vidu-mcp-server).