wavespeed-mcp 0.1.0

WaveSpeed MCP Server

WavespeedMCP

WavespeedMCP is a Model Control Protocol (MCP) server implementation for WaveSpeed AI services. It provides a standardized interface for accessing WaveSpeed's image and video generation capabilities through the MCP protocol.

Features

• Image Generation: Create high-quality images from text prompts or modify existing images
• Video Generation: Transform static images into dynamic videos with customizable parameters
• Modular Architecture: Clean, maintainable code structure with clear separation of concerns
• Robust Error Handling: Comprehensive exception handling and logging
• Flexible Configuration: Support for environment variables and configuration files

Installation

Prerequisites

• Python 3.8+
• WaveSpeed API key (obtain from WaveSpeed AI)

Setup

1. Clone the repository:

   git clone https://github.com/your-organization/wavespeed-mcp.git
   cd wavespeed-mcp
   

2. Install the package:

   pip install -e .
   

3. Create a .env file with your API credentials (see .env.example for reference):

   WAVESPEED_API_KEY=your_api_key_here
   WAVESPEED_API_HOST=https://api.wavespeed.ai
   

Usage

Running the Server

Start the WavespeedMCP server:

python -m wavespeed_mcp --api-key your_api_key_here

Or, if installed via pip:

wavespeed-mcp --api-key your_api_key_here

Client Application

Use the included client application to interact with the server:

python -m wavespeed_mcp.client_app --tool generate_image --params '{"prompt": "A beautiful sunset over mountains"}'

Or, if installed via pip:

wavespeed-mcp-client --tool generate_image --params '{"prompt": "A beautiful sunset over mountains"}'

API Reference

Image Generation

Generate images from text descriptions or modify existing images:

from mcp import ClientSession
from mcp.client.stdio import stdio_client
from mcp import StdioServerParameters

async def generate_image():
    server_params = StdioServerParameters(
        command="python",
        args=["-m", "wavespeed_mcp", "--api-key", "your_api_key_here"]
    )
    
    async with stdio_client(server_params) as (read_stream, write_stream):
        async with ClientSession(read_stream, write_stream) as client:
            await client.initialize()
            
            params = {
                "prompt": "A beautiful mountain landscape",
                "size": "1024*1024",
                "num_images": 1
            }
            
            result = await client.call_tool("generate_image", params)
            
            # Process result
            for item in result:
                if item.type == "image":
                    print(f"Generated image URL: {item.url}")

Video Generation

Generate videos from static images:

async def generate_video():
    # ... setup client as above ...
    
    params = {
        "image_url": "https://example.com/image.jpg",
        "prompt": "A scenic landscape with gentle movement",
        "duration": 5
    }
    
    result = await client.call_tool("generate_video", params)
    
    # Process result
    for item in result:
        if item.type == "text":
            print(item.text)  # Contains URL to generated video

Configuration

WavespeedMCP can be configured through:

1. Environment variables (see .env.example)
2. Command-line arguments
3. Configuration file (see wavespeed_mcp_config_demo.json)

Architecture

WavespeedMCP follows a clean, modular architecture:

• server.py: Core MCP server implementation with tool definitions
• client.py: API client for communicating with WaveSpeed services
• utils.py: Helper functions for file handling and other utilities
• exceptions.py: Custom exception classes for error handling
• const.py: Constants and default values

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.