mcp-ocr 0.2.0

MCP server for OCR functionality using Tesseract

MCP OCR Server

A production-grade OCR server built using MCP (Model Context Protocol) that provides OCR capabilities through a simple interface.

Features

• Extract text from images using Tesseract OCR
• Support for multiple input types:
  • Local image files
  • Image URLs
  • Raw image bytes
• Automatic Tesseract installation
• Support for multiple languages
• Production-ready error handling

Installation

# Using pip
pip install mcp-ocr

# Using uv
uv pip install mcp-ocr

Tesseract will be installed automatically on supported platforms:

• macOS (via Homebrew)
• Linux (via apt, dnf, or pacman)
• Windows (manual installation instructions provided)

Usage

As an MCP Server

1. Start the server:

python -m mcp_ocr

2. Configure Claude for Desktop: Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
    "mcpServers": {
        "ocr": {
            "command": "python",
            "args": ["-m", "mcp_ocr"]
        }
    }
}

Available Tools

perform_ocr

Extract text from images:

# From file
perform_ocr("/path/to/image.jpg")

# From URL
perform_ocr("https://example.com/image.jpg")

# From bytes
perform_ocr(image_bytes)

get_supported_languages

List available OCR languages:

get_supported_languages()

Development

1. Clone the repository:

git clone https://github.com/rjn32s/mcp-ocr.git
cd mcp-ocr

2. Set up development environment:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .

3. Run tests:

pytest

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

Security

• Never commit API tokens or sensitive credentials
• Use environment variables or secure credential storage
• Follow GitHub's security best practices

License

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

Acknowledgments

• Tesseract OCR
• Model Context Protocol