WebP Batch Converter

Batch convert PNG, JPG, and JPEG images to WebP format with options for quality, lossless mode, and multi-threaded processing.

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WebP Batch Converter

A Model Context Protocol (MCP) server for batch converting images to WebP format with cross-platform support. Works seamlessly with MCP-aware IDEs like Cursor.

🌟 Features

  • 🖼️ Batch conversion of PNG, JPG, and JPEG files to WebP
  • 🌍 Cross-platform support (macOS, Linux, Windows)
  • ⚡ Multi-threaded processing for fast conversions
  • 🎛️ Flexible options including quality control, lossless mode, and metadata preservation
  • 📊 Detailed reporting with file sizes and savings statistics
  • 🔧 Dual engine support - prefers Google's cwebp, falls back to Sharp
  • 🎯 MCP integration for use in AI-powered development environments

📦 Installation

Global Installation

npm install -g webp-batch-mcp

Local Development

git clone https://github.com/mhe8mah/webp-batch-mcp.git
cd webp-batch-mcp
npm install
npm run build

Docker

docker build -t webp-batch .
docker run -v /path/to/images:/data webp-batch

🚀 Usage

Command Line Interface

node dist/cli.js [options]

Options

  • --src <dir> - Source directory to scan (default: current directory)
  • --quality <0-100> - WebP quality setting (default: 75)
  • --lossless - Use lossless encoding (recommended for PNG)
  • --overwrite - Replace original files with WebP versions
  • --threads <n> - Number of concurrent conversions (default: CPU count)
  • --preserve-meta - Preserve EXIF and ICC metadata
  • --flat <dir> - Output all WebP files to specified directory

Examples

# Convert all images in current directory
node dist/cli.js

# High quality conversion of specific directory
node dist/cli.js --src ./photos --quality 95 --preserve-meta

# Lossless conversion with overwrite
node dist/cli.js --src ./images --lossless --overwrite

# Batch process to output directory
node dist/cli.js --src ./input --flat ./output --threads 8

MCP Server

The MCP server exposes a single tool: convert_to_webp

Tool Parameters

{
  "src": "string",          // Source directory (default: ".")
  "quality": "number",      // Quality 0-100 (default: 75)
  "lossless": "boolean",    // Lossless mode (default: false)
  "overwrite": "boolean",   // Replace originals (default: false)
  "threads": "number",      // Concurrent threads (default: CPU count)
  "preserveMeta": "boolean", // Keep metadata (default: false)
  "flat": "string"          // Output directory (optional)
}

⚙️ How to Add This Server in Cursor

  1. Clone and build the project:
git clone https://github.com/mhe8mah/webp-batch-mcp.git
cd webp-batch-mcp
npm install
npm run build
  1. Open Cursor Settings
  2. Navigate to Features → MCP
  3. Add a new server configuration:
{
  "mcpServers": {
    "webp-batch": {
      "command": "node",
      "args": ["/path/to/webp-batch-mcp/dist/server.js"]
    }
  }
}
  1. Restart Cursor
  2. The convert_to_webp tool will be available in your AI conversations

🔧 Technical Details

Conversion Strategy

  1. Primary Engine: Google's cwebp tool (included in libwebp-tools)

    • Fastest performance
    • Best compression
    • Full feature support

  2. Fallback Engine: Sharp (Node.js)

    • Pure JavaScript implementation
    • No external dependencies
    • Cross-platform compatibility

Output Behavior

  • Default: Creates .webp files alongside originals
  • Overwrite mode: Replaces originals with WebP versions
  • Flat mode: Outputs all WebP files to specified directory
  • Metadata preservation: Maintains EXIF and ICC profiles when requested

Performance

  • Utilizes all CPU cores by default
  • Processes images concurrently using p-limit
  • Provides real-time progress feedback
  • Reports detailed conversion statistics

🛠️ Development

Building

npm run build

Testing

npm test

Development Mode

npm run dev

📊 Test Results

Verified with real web images:

  • JPEG (35KB → 17KB): 51% space savings
  • PNG (7.9KB → 2.8KB): 65% space savings
  • Overall: 53% average compression

📋 Dependencies

Runtime

  • @modelcontextprotocol/sdk - MCP server framework
  • sharp - Image processing fallback
  • chalk - Colorized terminal output
  • commander - CLI argument parsing
  • glob - File pattern matching
  • p-limit - Concurrency control

Development

  • typescript - Type safety
  • tsup - Fast TypeScript bundler
  • jest - Testing framework

📄 License

MIT License - see LICENSE file for details.

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Add tests for new functionality
  4. Ensure all tests pass
  5. Submit a pull request

🆘 Support

For issues and feature requests, please use the GitHub issue tracker.

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