MCP Image Recognition Server

@mario-andreschak

Basic information

About MCP Image Recognition Server

An MCP server that provides image recognition 👀 capabilities using Anthropic and OpenAI vision APIs

MCP Image Recognition Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-image-recognition": {
      "command": "python",
      "args": [
        "-m",
        "image_recognition_server.server"
      ]
    }
  }
}

MCP Image Recognition Server tools

Base64-encoded image data and MIME type

Detailed description of the image

Path to an image file

Detailed description of the image

Overview

What is MCP Image Recognition Server?

An MCP server that provides image recognition capabilities using Anthropic and OpenAI vision APIs. It supports multiple image formats and optional OCR text extraction via Tesseract.

How to use MCP Image Recognition Server?

Clone the repository, copy .env.example to .env, set API keys and preferences, then build and run via python -m image_recognition_server.server or run.bat server. Use tools describe_image (base64 input) and describe_image_from_file (file path). Environment variables control provider, model, OCR, and logging.

Key features of MCP Image Recognition Server

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

Use cases of MCP Image Recognition Server

Automatically describing images in chat or workflow applications
Extracting text from images using optional OCR
Integrating vision capabilities into MCP-powered tools
Reliable image analysis with fallback provider

Frequently asked questions

What are the runtime requirements?

Python 3.8 or higher is required. Tesseract OCR is optional and only needed for text extraction.

How do I configure the vision provider?

Set `VISION_PROVIDER` to `anthropic` or `openai` in the `.env` file. Optionally set `FALLBACK_PROVIDER` for a secondary provider.

Can I use models other than the defaults?

Yes, via OpenRouter: set `OPENAI_BASE_URL` to `https://openrouter.ai/api/v1`, `OPENAI_MODEL` to the desired model (e.g., `anthropic/claude-3.5-sonnet:beta`), and `VISION_PROVIDER` to `openai`.

Does the server support Docker?

Yes. Build the image with `docker build -t mcp-image-recognition .` and run with `docker run -it --env-file .env mcp-image-recognition`.

What image formats are supported?

JPEG, PNG, GIF, and WebP are supported for both base64 and file-based input.

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