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.

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

hljs language-text

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

hljs language-bash

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

2. Configure

hljs language-bash

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):

hljs language-env

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):

hljs language-json

{
  "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

OpenCode Zen (MiMo-V2.5 Free) - Recommended free option

hljs language-env

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

Zhipu GLM-4V

hljs language-env

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

Qwen-VL via local proxy

hljs language-env

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

OpenAI GPT-4o

hljs language-env

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

Usage

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

hljs language-arduino

"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

hljs language-graphql

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

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.

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

hljs language-text

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

hljs language-bash

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

2. Configure

hljs language-bash

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):

hljs language-env

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):

hljs language-json

{
  "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

OpenCode Zen (MiMo-V2.5 Free) - Recommended free option

hljs language-env

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

Zhipu GLM-4V

hljs language-env

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

Qwen-VL via local proxy

hljs language-env

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

OpenAI GPT-4o

hljs language-env

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

Usage

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

hljs language-arduino

"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

hljs language-graphql

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

vision-mcp

vision-mcp

One-line Summary

Architecture

Features

Supported Vision APIs

Quick Start

1. Clone

2. Configure

3. Register in opencode

4. Restart opencode

Configuration

Provider Examples

Usage

Directory Structure

License

Similar Packages

vision-mcp

vision-mcp

One-line Summary

Architecture

Features

Supported Vision APIs

Quick Start

1. Clone

2. Configure

3. Register in opencode

4. Restart opencode

Configuration

Provider Examples

Usage

Directory Structure

License

Similar Packages