What is Mcp Image Recognition

mcp-image-recognition is an MCP server that provides image recognition capabilities using the vision APIs from Anthropic and OpenAI.

Use cases

Use cases for mcp-image-recognition include automated image tagging, content moderation, accessibility enhancements through image descriptions, and extracting text from images for further processing.

How to use

To use mcp-image-recognition, clone the repository, configure your environment file with API keys, and run the server using Python. You can describe images by providing either Base64-encoded data or file paths.

Key features

Key features include 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, and optional text extraction using Tesseract OCR.

Where to use

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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

2. Create and configure your environment file:

cp .env.example .env
# Edit .env with your API keys and preferences

3. 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