👁️ Vision MCP Server

Give your AI agent eyes. An MCP server providing multimodal vision capabilities — image analysis, OCR, image comparison, and video analysis — powered by any OpenAI-compatible vision model.

让你的 AI 代理拥有视觉能力。 通过任何 OpenAI 兼容的视觉模型，提供图像分析、OCR 文字识别、图像对比和视频分析。

Features · Quick Start · Tools · Models · 中文说明

✨ Features

Plus:

• 🌐 OpenAI-compatible — Works with any vision model via standard API
• 📁 Local files & URLs — Auto-converts local files to base64
• ⚙️ Configurable — Environment variables, config files, or both

🚀 Quick Start

1. Install

git clone https://github.com/Loveacup/vision-mcp-server.git
cd vision-mcp-server
npm install && npm run build

2. Configure

Create a .env file in the project root:

VISION_BASE_URL=http://your-server:port/v1/chat/completions
VISION_MODEL=Qwen3-VL-32B
VISION_API_KEY=your-api-key    # optional for local models

{
  "baseUrl": "http://your-server:port/v1/chat/completions",
  "model": "Qwen3-VL-32B",
  "apiKey": "your-api-key",
  "maxTokens": 4096,
  "temperature": 0.7
}

3. Run

npm start

The server communicates over stdio, designed to be launched by an MCP client such as Claude Code.

🔌 Claude Code Integration

Add to your ~/.mcp.json:

{
  "mcpServers": {
    "vision": {
      "command": "node",
      "args": ["/path/to/vision-mcp-server/dist/index.js"],
      "env": {
        "VISION_BASE_URL": "http://your-server:port/v1/chat/completions",
        "VISION_MODEL": "Qwen3-VL-32B",
        "VISION_API_KEY": "your-api-key"
      }
    }
  }
}

Replace /path/to/vision-mcp-server with the actual install path.

⚙️ Configuration Reference

Configuration priority: environment variables > config file > defaults

🛠️ Tools Reference

analyze_image

Analyze an image with a vision language model.

ocr_image

Extract text from an image using OCR.

compare_images

Compare 2–4 images and describe differences/similarities.

analyze_video

Analyze video content. Requires a model with video support (e.g., Qwen3-VL).

🤖 Supported Models

Any model served via vLLM, Ollama, LMDeploy, or other OpenAI-compatible servers should work.

Supported formats: JPEG, PNG, GIF, WebP, BMP, SVG | MP4, AVI, MOV, MKV, WebM

📁 Project Structure

vision-mcp-server/
├── src/
│   ├── index.ts              # MCP server entry point
│   ├── config.ts             # Configuration loader
│   ├── types.ts              # TypeScript type definitions
│   ├── tools/
│   │   ├── analyze-image.ts
│   │   ├── ocr-image.ts
│   │   ├── compare-images.ts
│   │   └── analyze-video.ts
│   └── utils/
│       ├── api-client.ts     # OpenAI-compatible API client
│       └── file-handler.ts   # Local file → base64
├── package.json
├── tsconfig.json
├── .env.example
└── LICENSE

📄 License

MIT

🇨🇳 中文说明

功能

• analyze_image — 使用视觉语言模型分析图像，支持自然语言提问
• ocr_image — OCR 文字识别，支持纯文本、Markdown、JSON 输出
• compare_images — 对比 2–4 张图像，识别差异和相似之处
• analyze_video — 分析视频内容（需要 Qwen3-VL 等支持视频的模型）

快速开始

git clone https://github.com/Loveacup/vision-mcp-server.git
cd vision-mcp-server
npm install && npm run build

配置 .env：

VISION_BASE_URL=http://your-server:port/v1/chat/completions
VISION_MODEL=Qwen3-VL-32B
VISION_API_KEY=your-api-key

在 Claude Code 的 ~/.mcp.json 中添加：

{
  "mcpServers": {
    "vision": {
      "command": "node",
      "args": ["/path/to/vision-mcp-server/dist/index.js"],
      "env": {
        "VISION_BASE_URL": "http://your-server:port/v1/chat/completions",
        "VISION_MODEL": "Qwen3-VL-32B",
        "VISION_API_KEY": "your-api-key"
      }
    }
  }
}

将 /path/to/vision-mcp-server 替换为实际安装路径。