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MCP Image Recognition Server

MCP Image Recognition Server

Local
github.com/mario-andreschakMedia & Design40Updated 81w ago
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About MCP Image Recognition Server

MCP Image Recognition Server is an MCP server that describes images using Anthropic Claude Vision or OpenAI vision models. It suits users who need image understanding in MCP clients, with primary and fallback provider settings and optional Tesseract OCR text extraction.

Key features

  1. 01Image descriptions from Anthropic or OpenAI vision models
  2. 02Configurable primary and fallback providers
  3. 03Base64 and file-path image input
  4. 04Optional Tesseract OCR text extraction

Use cases

  1. 01Adding image content descriptions to MCP client workflows
  2. 02Extracting text from images alongside descriptions
  3. 03Using OpenRouter to access other vision models

MCP support

This is an MCP server that exposes the describe_image and describe_image_from_file tools, returning detailed descriptions of images to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project 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

Comments

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