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MCP Image Recognition Server

@mario-andreschak

代码仓库

基本信息

分类

媒体与设计

许可证

MIT

传输方式

stdio

作者

mario-andreschak

关于 MCP Image Recognition Server

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

MCP Image Recognition Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

MCP Image Recognition Server 工具

4

Base64-encoded image data and MIME type

Detailed description of the image

Path to an image file

Detailed description of the image

概览

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

常见问题

1

What are the runtime requirements?

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

2

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.

3

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`.

4

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`.

5

What image formats are supported?

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

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