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MCP Image Recognition Server

@mario-andreschak

Repository

About MCP Image Recognition Server

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

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"
      ]
    }
  }
}

Tools

4

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

FAQ from MCP Image Recognition Server

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.

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.

Comments

Basic information

Category

Media & Design

License

MIT

Transports

stdio

Author

mario-andreschak

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