Rembg MCP Server

An MCP (Model Context Protocol) server for the rembg background removal library. Remove image backgrounds using AI models through Claude Code, Claude Desktop, Cursor, and other MCP-compatible tools.

🎯 Features

• 🖼️ Image Processing: Remove backgrounds from single images or batch process folders

• 🤖 Multiple AI Models: u2net, birefnet, isnet, sam, and more specialized models

• ⚡ Performance Optimized: Model session reuse for efficient batch processing

• 🎨 Advanced Options: Alpha matting, mask-only output, custom backgrounds

• 🌍 Cross-Platform: Support for Windows, macOS, and Linux

• 🔧 Easy Integration: Works with Claude Desktop, Claude Code CLI, Cursor IDE

📦 Quick Start

🚀 One-Click Installation

Linux/macOS

Windows

The setup scripts will automatically:

• Check Python 3.10+ requirement

• Create virtual environment

• Install all dependencies

• Configure MCP server

• Test the installation

• Guide you through AI model downloads

🔧 Manual Installation

If you prefer manual installation or need custom configuration:

1. Create virtual environment:

2. Install dependencies:

3. Test installation:

4. Download AI models:

5. For GPU support:

🔧 MCP Configuration

Claude Desktop Setup

1. Find your Claude Desktop config file:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

   • Windows: %APPDATA%\Claude\claude_desktop_config.json

   • Linux: ~/.config/Claude/claude_desktop_config.json

2. Add the rembg server configuration:

3. Replace with your actual project path

4. Restart Claude Desktop

Testing Your Setup

After configuration, test your MCP server:

1. Start the server manually:

2. Verify MCP connection in Claude Desktop:

   • Look for the rembg tools in your Claude conversation

   • Try a simple command: "List available MCP tools"

3. Test with a sample image:

   • Ask Claude: "Use rembg-i to remove the background from test.jpg"

   • The server will process your request and return results

Claude Code CLI Setup

Add to your Claude Code settings:

Cursor IDE Setup

Add to your Cursor settings or workspace .cursor/settings.json:

Windows Configuration

For Windows users, use start_server.bat instead:

🚀 How to Use

Once configured, you can use the rembg tools directly in your MCP-compatible application:

Basic Usage Examples

Single Image Processing:

Batch Processing:

Advanced Processing:

🛠️ Available MCP Tools

rembg-i - Single Image Background Removal

Removes background from a single image file with high precision.

Required Parameters:

• input_path: Path to the source image file

• output_path: Where to save the processed image

Optional Parameters:

• model: AI model to use (default: "u2net")

• alpha_matting: Improve edge quality (default: false)

• only_mask: Output black/white mask only (default: false)

Supported formats: JPG, PNG, BMP, TIFF, WebP

rembg-p - Batch Folder Processing

Processes all images in a folder automatically.

Required Parameters:

• input_folder: Source folder containing images

• output_folder: Destination folder for processed images

Optional Parameters:

• model: AI model to use (default: "u2net")

• alpha_matting: Improve edge quality (default: false)

• only_mask: Output masks only (default: false)

• file_extensions: File types to process (default: common image formats)

Features:

• Automatically finds all supported images

• Preserves original filenames with .out.png suffix

• Detailed progress reporting

• Error handling for individual files

🤖 Supported AI Models

🎯 Model Recommendations

For beginners: Start with u2net (default) - good balance of speed and quality

For best quality: Use birefnet-general or birefnet-massive

For portraits: Use birefnet-portrait - specialized for human subjects

For anime/cartoons: Use isnet-anime - optimized for animated content

For speed: Use u2netp or silueta - faster processing for batch jobs

📥 Downloading Models

Models are downloaded automatically when first used, but you can pre-download them:

Models are cached in ~/.u2net/ and only need to be downloaded once.

🔧 Configuration

Environment Variables

• REMBG_HOME: Model storage directory (default: ~/.u2net)

• OMP_NUM_THREADS: Number of CPU threads for processing (default: 4)

• MODEL_CHECKSUM_DISABLED: Skip model checksum verification

Advanced Options

• Alpha Matting: Improves edge quality but increases processing time

• Mask Only: Returns black/white mask instead of transparent cutout

• Custom Background Colors: Replace transparent areas with solid colors

• Batch Processing: Automatically reuses model sessions for efficiency

📁 Project Structure

🚨 Troubleshooting

Common Issues

MCP Server Not Found

• Verify the command path in your MCP configuration

• Ensure the script is executable: chmod +x start_server.sh

• Check that the virtual environment exists: ls rembg/

Python Version Issues

Model Download Problems

Memory or Performance Issues

Installation Problems

Getting Help

• Run python validate_setup.py for detailed diagnostics

• Check server logs when starting manually

• Ensure your MCP client supports the latest protocol version

📚 Additional Resources

• Rembg Library Documentation

• MCP Protocol Specification

• Claude Code Documentation

🤝 Contributing

1. Fork the repository

2. Create your feature branch (git checkout -b feature/amazing-feature)

3. Commit your changes (git commit -m 'Add amazing feature')

4. Push to the branch (git push origin feature/amazing-feature)

5. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• danielgatis/rembg - The excellent background removal library

• Anthropic - For the MCP protocol and Claude

• The open source community for the various AI models