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mcp-image-recognition

MCP.Pizza Chef: mario-andreschak

The mcp-image-recognition server provides advanced image recognition capabilities by integrating Anthropic Claude Vision and OpenAI GPT-4 Vision APIs. It supports multiple image formats including JPEG, PNG, GIF, and WebP, and offers configurable primary and fallback providers. The server accepts both base64 and file-based image inputs and optionally extracts text using Tesseract OCR, making it versatile for various image analysis workflows.

Use This MCP server To

Describe images using AI vision models from Anthropic and OpenAI Extract text from images with optional Tesseract OCR integration Process multiple image formats for recognition and analysis Configure fallback providers for reliable image recognition Integrate image recognition into MCP-enabled applications Support base64 and file input for flexible image processing

README

MCP Image Recognition Server

An MCP server that provides image recognition capabilities using Anthropic and OpenAI vision APIs. Version 0.1.2.

Features

  • Image description using Anthropic Claude Vision or OpenAI GPT-4 Vision
  • Support for multiple image formats (JPEG, PNG, GIF, WebP)
  • Configurable primary and fallback providers
  • Base64 and file-based image input support
  • Optional text extraction using Tesseract OCR

Requirements

  • Python 3.8 or higher
  • Tesseract OCR (optional) - Required for text extraction feature
    • Windows: Download and install from UB-Mannheim/tesseract
    • Linux: sudo apt-get install tesseract-ocr
    • macOS: brew install tesseract

Installation

  1. Clone the repository:
git clone https://github.com/mario-andreschak/mcp-image-recognition.git
cd mcp-image-recognition
  1. Create and configure your environment file:
cp .env.example .env
# Edit .env with your API keys and preferences
  1. Build the project:
build.bat

Usage

Running the Server

Spawn the server using python:

python -m image_recognition_server.server

Start the server using batch instead:

run.bat server

Start the server in development mode with the MCP Inspector:

run.bat debug

Available Tools

  1. describe_image

    • Input: Base64-encoded image data and MIME type
    • Output: Detailed description of the image

  2. describe_image_from_file

    • Input: Path to an image file
    • Output: Detailed description of the image

Environment Configuration

  • ANTHROPIC_API_KEY: Your Anthropic API key.
  • OPENAI_API_KEY: Your OpenAI API key.
  • VISION_PROVIDER: Primary vision provider (anthropic or openai).
  • FALLBACK_PROVIDER: Optional fallback provider.
  • LOG_LEVEL: Logging level (DEBUG, INFO, WARNING, ERROR).
  • ENABLE_OCR: Enable Tesseract OCR text extraction (true or false).
  • TESSERACT_CMD: Optional custom path to Tesseract executable.
  • OPENAI_MODEL: OpenAI Model (default: gpt-4o-mini). Can use OpenRouter format for other models (e.g., anthropic/claude-3.5-sonnet:beta).
  • OPENAI_BASE_URL: Optional custom base URL for the OpenAI API. Set to https://openrouter.ai/api/v1 for OpenRouter.
  • OPENAI_TIMEOUT: Optional custom timeout (in seconds) for the OpenAI API.

Using OpenRouter

OpenRouter allows you to access various models using the OpenAI API format. To use OpenRouter, follow these steps:

  1. Obtain an OpenAI API key from OpenRouter.
  2. Set OPENAI_API_KEY in your .env file to your OpenRouter API key.
  3. Set OPENAI_BASE_URL to https://openrouter.ai/api/v1.
  4. Set OPENAI_MODEL to the desired model using the OpenRouter format (e.g., anthropic/claude-3.5-sonnet:beta).
  5. Set VISION_PROVIDER to openai.

Default Models

  • Anthropic: claude-3.5-sonnet-beta
  • OpenAI: gpt-4o-mini
  • OpenRouter: Use the anthropic/claude-3.5-sonnet:beta format in OPENAI_MODEL.

Development

Running Tests

Run all tests:

run.bat test

Run specific test suite:

run.bat test server
run.bat test anthropic
run.bat test openai

Docker Support

Build the Docker image:

docker build -t mcp-image-recognition .

Run the container:

docker run -it --env-file .env mcp-image-recognition

License

MIT License - see LICENSE file for details.

Release History

  • 0.1.2 (2025-02-20): Improved OCR error handling and added comprehensive test coverage for OCR functionality
  • 0.1.1 (2025-02-19): Added Tesseract OCR support for text extraction from images (optional feature)
  • 0.1.0 (2025-02-19): Initial release with Anthropic and OpenAI vision support

mcp-image-recognition FAQ

How do I install the mcp-image-recognition server?
Clone the GitHub repo, install Python 3.8+, and optionally install Tesseract OCR for text extraction.
Which image formats does this server support?
It supports JPEG, PNG, GIF, and WebP image formats.
Can I use multiple vision API providers?
Yes, you can configure primary and fallback providers like Anthropic Claude Vision and OpenAI GPT-4 Vision.
Is text extraction from images supported?
Yes, optional text extraction is available using Tesseract OCR.
What input types are accepted for image recognition?
Both base64-encoded images and file-based images are supported.
What are the system requirements?
Python 3.8 or higher is required; Tesseract OCR is optional for text extraction.
How do I configure API keys for the providers?
API keys are set in the environment file (.env) as per the installation instructions.
Can this server be integrated with other MCP clients?
Yes, it is designed to be used as an MCP server for image recognition in MCP-enabled workflows.