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MCP Image Recognition Server

@mario-andreschak

リポジトリ

MCP Image Recognition Server について

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

基本情報

カテゴリ

メディアとデザイン

ライセンス

MIT

ランタイム

python

トランスポート

stdio

公開者

mario-andreschak

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

4

Base64-encoded image data and MIME type

Detailed description of the image

Path to an image file

Detailed description of the image

概要

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.

コメント

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