What can you do with this server?

The MCP Image Recognition Server allows you to analyze images using various AI vision models with these capabilities: * **Describe images** from base64-encoded data, local file paths, or public URLs * **Support multiple image formats** including JPEG, PNG, GIF, WebP * **Choose between AI providers**: Anthropic, OpenAI, and Cloudflare Workers AI with configurable primary and fallback options * **Optional Tesseract OCR integration** for text extraction from images * **Customizable prompts** to guide the image description process * **Flexible deployment options**: local, Docker, or via uvx/pip installation * **Integration with MCP-compatible clients** like Claude Desktop and Cursor

Which integrations are available for this server?

Hosts the MCP server repository, allowing users to clone the codebase for local deployment and configuration Utilizes [OpenAI](/mcp/servers/integrations/openai) GPT-4 Vision API for image analysis and detailed descriptions from both base64-encoded images and image files

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

Related MCP server: Google 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

Clone the repository:

git clone https://github.com/mario-andreschak/mcp-image-recognition.git cd mcp-image-recognition

Create and configure your environment file:

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

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

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

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