Which integrations are available for this server?

Enables image analysis, OCR, image comparison, and video analysis using local vision models served by Ollama. Provides tools for image analysis, OCR, image comparison, and video analysis using OpenAI-compatible vision models.

How do I use Vision MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Vision MCP Server extract text from this image: receipt.jpg" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Vision MCP Server

by Loveacup

Overview Schema Related Servers Score Discussions

TypeScript

Remote

👁️ Vision MCP Server

License: MIT Node.js MCP

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

Tool	Description
🔍 `analyze_image`	Analyze images with natural language prompts
📝 `ocr_image`	Extract text from images (plain text / Markdown / JSON)
🔀 `compare_images`	Compare 2–4 images side by side
🎬 `analyze_video`	Analyze video content (requires video-capable model)

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

Related MCP server: mcp-see

🚀 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

Variable	Config Key	Default	Description
`VISION_BASE_URL`	`baseUrl`	(required)	OpenAI-compatible chat completions endpoint
`VISION_MODEL`	`model`	`Qwen3-VL-32B`	Model name
`VISION_API_KEY`	`apiKey`	(empty)	API key (optional for local models)
`VISION_MAX_TOKENS`	`maxTokens`	`4096`	Max response tokens
`VISION_TEMPERATURE`	`temperature`	`0.7`	Sampling temperature

🛠️ Tools Reference

`analyze_image`

Analyze an image with a vision language model.

Parameter	Type	Required	Default	Description
`image`	string	✅	—	Local file path or URL
`prompt`	string		`"Describe this image in detail."`	Analysis prompt
`detail`	`"low"` \| `"high"` \| `"auto"`		`"auto"`	Detail level

`ocr_image`

Extract text from an image using OCR.

Parameter	Type	Required	Default	Description
`image`	string	✅	—	Local file path or URL
`languages`	string		`""`	Language hint, e.g. `"zh,en"`
`format`	`"plain"` \| `"markdown"` \| `"json"`		`"plain"`	Output format

`compare_images`

Compare 2–4 images and describe differences/similarities.

Parameter	Type	Required	Default	Description
`images`	string[]	✅	—	2–4 image sources
`prompt`	string		`"Compare these images..."`	Comparison prompt

`analyze_video`

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

Parameter	Type	Required	Default	Description
`video`	string	✅	—	Local file path or URL
`prompt`	string		`"Describe what happens in this video."`	Analysis prompt

🤖 Supported Models

Model	Provider	Image	Video	Notes
Qwen3-VL	Self-hosted / API	✅	✅	Recommended. Full multimodal support
GPT-4o	OpenAI	✅	❌	Strong image analysis
LLaVA	Self-hosted	✅	❌	Open-source alternative
InternVL	Self-hosted	✅	⚠️	Strong multilingual OCR

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。

Install Server

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