vision-mcp

vision-mcp

Enables AI agents to analyze images using any OpenAI-compatible vision API, providing tools for image analysis, OCR, error diagnosis, diagram understanding, and chart analysis.

Category
访问服务器

README

vision-mcp

Vision MCP Server for AI Agents - give your text-only model eyes.

Use any OpenAI-compatible vision model API through MCP tools for image analysis, OCR, error diagnosis, diagram understanding, and chart analysis.

License: MIT Version

中文文档


One-line Summary

Give AI Agents visual understanding through MCP tools. Works with any OpenAI-compatible vision API (GLM-4V, Qwen-VL, MiMo-V2.5, GPT-4o, etc.).

Analogy: Vision MCP is the eyes for text-only models like GLM-5.1.


Architecture

User sends image path or URL
  -> AI Agent (text model, e.g. GLM-5.1)
  -> vision-mcp tools (MCP)
  -> Vision Model API (e.g. MiMo-V2.5-Free, Qwen3-VL, GLM-4V)
  -> Returns text description
  -> AI Agent continues with understanding

Features

Tool Description
image_analysis General image understanding
image_analysis_url Analyze image from URL
extract_text OCR - extract text from screenshots
diagnose_error Analyze error screenshots, suggest fixes
understand_diagram Architecture/flow/UML diagram analysis
analyze_chart Chart and data visualization analysis

Supported Vision APIs

Provider Model API Base Cost
OpenCode Zen (MiMo-V2.5) mimo-v2.5-free https://opencode.ai/zen/v1 Free
Zhipu (GLM-4V) glm-4v https://open.bigmodel.cn/api/paas/v4 Paid
Qwen-VL (via proxy) Qwen3-VL-ms Your proxy URL Varies
OpenAI gpt-4o https://api.openai.com/v1 Paid
Any OpenAI-compatible Custom Custom Varies

MiMo-V2.5 is a native omnimodal model by Xiaomi that supports text, image, video, and audio. The free tier on OpenCode Zen is a great starting point.


Quick Start

1. Clone

git clone https://github.com/knownothing20/vision-mcp.git
cd vision-mcp
npm install

2. Configure

cp local/.env.example local/.env
# Edit local/.env, fill in your API key and model

Minimal config for MiMo-V2.5 Free (OpenCode Zen):

VISION_API_KEY=your-opencode-zen-api-key
VISION_API_BASE=https://opencode.ai/zen/v1
VISION_MODEL=mimo-v2.5-free

3. Register in opencode

Add to opencode.json (replace path with your actual skill dir):

{
  "mcp": {
    "vision-mcp": {
      "command": ["node", "/path/to/vision-mcp/index.js"],
      "enabled": true,
      "type": "local"
    }
  }
}

Or use node sync.cjs to auto-register (recommended).

4. Restart opencode

Restart your AI tool to activate MCP registration.


Configuration

Edit local/.env:

Variable Description Default
VISION_API_KEY API key (required) -
VISION_API_BASE API endpoint https://open.bigmodel.cn/api/paas/v4
VISION_MODEL Model name glm-4v

Priority: local/.env > .env (root) > environment variables.

Provider Examples

<details> <summary>OpenCode Zen (MiMo-V2.5 Free) - Recommended free option</summary>

VISION_API_KEY=sk-your-zen-api-key
VISION_API_BASE=https://opencode.ai/zen/v1
VISION_MODEL=mimo-v2.5-free

Get your API key at https://opencode.ai/auth

</details>

<details> <summary>Zhipu GLM-4V</summary>

VISION_API_KEY=your-zhipu-api-key
VISION_API_BASE=https://open.bigmodel.cn/api/paas/v4
VISION_MODEL=glm-4v

</details>

<details> <summary>Qwen-VL via local proxy</summary>

VISION_API_KEY=your-proxy-key
VISION_API_BASE=http://192.168.x.x:8317/v1
VISION_MODEL=Qwen3-VL-ms

</details>

<details> <summary>OpenAI GPT-4o</summary>

VISION_API_KEY=your-openai-api-key
VISION_API_BASE=https://api.openai.com/v1
VISION_MODEL=gpt-4o

</details>


Usage

After setup, provide image file paths to your AI agent:

"Analyze this image: C:\Users\you\Desktop\screenshot.png"
"Extract text from /path/to/document.png"
"Diagnose this error: C:\screenshots\error.png"
"What does this architecture diagram mean? /path/to/diagram.png"
"Analyze chart trends: /path/to/chart.png"

Note: Your main AI model (e.g. GLM-5.1) may not support image input directly. Provide the file path instead of embedding the image in chat, and the agent will use vision-mcp tools to analyze it.


Directory Structure

vision-mcp/
├── index.js              # MCP server main program
├── sync.cjs              # Repo <-> skill dir sync tool
├── package.json          # Dependencies
├── SKILL.md              # Agent documentation
├── README.md             # This file
├── README_CN.md          # Chinese documentation
├── AGENT_GUIDE.md        # Agent install guide
├── INSTALL_OTHER_MACHINE.md  # Install on another machine
├── CHANGELOG.md          # Version changelog
├── LICENSE               # MIT License
├── .gitignore
└── local/                # Local private config (gitignored)
    └── .env.example      # Config template

License

MIT License - See LICENSE

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选