npx mcpbar@latest edit -c claude

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

Installing via Smithery

To install Vidu Video Generation Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @el-el-san/vidu-mcp-server --client claude

Manual Installation

1. Clone this repository:

git clone https://github.com/el-el-san/vidu-mcp-server.git
cd vidu-mcp-server

2. Install dependencies:

npm install

3. Create a .env file based on the .env.template and add your Vidu API key:

VIDU_API_KEY=your_api_key_here

Usage

1. Build the TypeScript code:

npm run build

2. 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.