MCP Image Tools Server

A Model Context Protocol (MCP) server that provides powerful image processing tools for Claude Code. This server implements three main functionalities: downloading toy-related images from the web, resizing images, and removing backgrounds from images.

• Anthropic MCP Pythone SDK Github repo: https://github.com/modelcontextprotocol/python-sdk?tab=readme-ov-file

Features

🧸 Toy Image Fetcher (fetch_toy_image)

• Downloads toy-related images from DuckDuckGo search
• Automatically prefixes search terms with "toy" for better results
• Supports downloading 1-10 images per request
• Saves images to a specified directory

🖼️ Image Resizer (resize_image)

• Resize images to specific dimensions
• Option to maintain aspect ratio
• High-quality resampling using Lanczos algorithm
• Support for all common image formats

✂️ Background Remover (remove_background_as_png)

• AI-powered background removal using state-of-the-art models
• Multiple model options (u2net, u2netp, silueta, isnet-general-use)
• Outputs PNG with transparent background
• Preserves main object details

Prerequisites

• Python 3.11 or higher
• Docker (for containerized deployment)
• Claude Code (for MCP client integration)

Installation

Option 1: Local Python Installation

1. Clone or create the project directory:

   mkdir mcp-toy-image-tools && cd mcp-toy-image-tools

2. Install Python dependencies:

   pip install -r requirements.txt

3. Run the server:

   python server.py

Option 2: Docker Installation (Recommended)

1. Build the Docker image:

   docker build -t mcp-toy-image-tools-server .

2. Create necessary directories:

   mkdir -p images input output

3. Run the container:

   docker run --rm -i \
     --name mcp-toy-image-tools \
     -v $(pwd)/images:/app/images \
     -v $(pwd)/input:/app/input \
     -v $(pwd)/output:/app/output \
     mcp-toy-image-tools-server

Claude Code Integration

Step 1: Configure Claude Code

1. Copy the MCP configuration to your Claude Code settings:

   For Docker execution:

   {
     "mcpServers": {
       "image-tools-server-docker": {
         "command": "docker",
         "args": [
           "run",
           "--rm",
           "-i",
           "--name", "mcp-toy-image-tools",
           "-v", "${PWD}/images:/app/images",
           "-v", "${PWD}/input:/app/input",
           "-v", "${PWD}/output:/app/output",
           "mcp-toy-image-tools-server"
         ],
         "cwd": "/path/to/your/mcp-toy-image-tools"
       }
     }
   }

2. Update the cwd path to match your actual project directory.

Step 2: Restart Claude Code

After updating your MCP configuration, restart Claude Code to load the new server.

Usage Examples

Once integrated with Claude Code, you can use these commands:

Download Toy Images

Please use the fetch_toy_image tool to download 5 robot toy images to the ./images directory.

Resize Images

Can you resize the image at ./images/robot_toy_1.jpg to 800x600 pixels?

Remove Background

Please remove the background from ./images/robot_toy_1.jpg and save it as a PNG.

File Structure

mcp-toy-image-tools/
├── server.py              # Main MCP server implementation
├── requirements.txt       # Python dependencies
├── Dockerfile            # Docker container configuration
├── .mcp.json            # Claude Code MCP configuration
├── README.md            # This documentation
├── images/              # Directory for downloaded/processed images
├── input/               # Directory for input images (Docker)
└── output/              # Directory for output images (Docker)

Dependencies

Python Libraries

• mcp: Anthropic's Model Context Protocol SDK
• Pillow: Python Imaging Library for image processing
• rembg: AI-powered background removal
• requests: HTTP client for downloading images
• duckduckgo-search: DuckDuckGo search API client
• torch/torchvision: PyTorch for AI model inference

System Dependencies (Docker only)

• OpenGL libraries for image processing
• GLib and threading libraries
• Various image format support libraries

Configuration Options

Environment Variables

• PYTHONPATH: Set to project directory for proper module resolution

Volume Mounts (Docker)

• /app/images: Directory for downloaded and processed images
• /app/input: Input directory for source images
• /app/output: Output directory for processed images

Troubleshooting

Common Issues

1. "duckduckgo-search library not available" error:

   pip install duckduckgo-search

2. Image download failures:

   • Check internet connection
   • Some images may be blocked by the source website
   • The tool automatically retries with additional results

3. Background removal model download:

   • First use may take longer as AI models are downloaded
   • Ensure sufficient disk space (~100MB+ for models)

4. Permission errors (Docker):

   • Ensure volume mount directories have proper permissions
   • The container runs as non-root user mcp-user

Debug Mode

To run with debug logging:

# Direct Python
PYTHONPATH=. python server.py --log-level DEBUG

# Docker
docker run --rm -i -e LOG_LEVEL=DEBUG mcp-toy-image-tools-server

Claude Code Connection Issues

1. Server not appearing in Claude Code:

   • Check that .mcp.json is in the correct location
   • Verify the cwd path is correct
   • Restart Claude Code after configuration changes

2. Tool execution errors:

   • Check server logs for detailed error messages
   • Ensure all dependencies are installed
   • Verify file paths are accessible

Development

Adding New Tools

To add new image processing tools:

1. Define the tool in handle_list_tools():

   Tool(
       name="your_new_tool",
       description="Description of what it does",
       inputSchema={...}
   )

2. Implement the handler in handle_call_tool():

   elif name == "your_new_tool":
       return await your_new_tool_function(arguments)

3. Add the async function implementation:

   async def your_new_tool_function(arguments: dict[str, Any]) -> list[TextContent]:
       # Implementation here
       pass

Testing

Test the server independently:

echo '{"method": "tools/list", "params": {}}' | python server.py

License

This project is provided as-is for educational and development purposes. Please respect the terms of service of image sources and AI models used.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

Support

For issues and questions:

• Check the troubleshooting section above
• Review Claude Code MCP documentation
• Submit issues to the project repository

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Note: This tool downloads images from the internet and uses AI models for processing. Please use responsibly and respect copyright and terms of service of source websites.