mcp-image-recognition

# MCP Image Recognition Server An MCP server that provides image recognition capabilities using Anthropic, OpenAI, and Cloudflare Workers AI vision APIs. Version 1.2.1.  ## Authors Originally this project was created by [`@mario-andreschak`](https://github.com/mario-andreschak/).^{Thank you!} It is currently maintained by [`@zudsniper`](https://github.com/zudsniper). ## Features - Image description using Anthropic Claude Vision, OpenAI GPT-4 Vision, or Cloudflare Workers AI llava-1.5-7b-hf - Easy integration with Claude Desktop, Cursor, and other MCP-compatible clients - Support for Docker deployment - Support for uvx installation - 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](https://github.com/UB-Mannheim/tesseract/wiki) - Linux: `sudo apt-get install tesseract-ocr` - macOS: `brew install tesseract` ## Installation ### Option 1: Using uvx (Recommended for Claude Desktop and Cursor) 1. Install [uv](https://github.com/astral-sh/uv) package manager: ```bash pip install uv ``` 2. Install the package with uvx: ```bash uvx install mcp-image-recognition ``` 3. Create and configure your environment file as described in the Configuration section ### Option 2: Using Docker ```bash docker pull zudsniper/mcp-image-recognition:latest # Create a .env file first, then run: docker run -it --env-file .env zudsniper/mcp-image-recognition ``` ### Option 3: From Source 1. Clone the repository: ```bash git clone https://github.com/zudsniper/mcp-image-recognition.git cd mcp-image-recognition ``` 2. Create and configure your environment file: ```bash cp .env.example .env # Edi…