Which integrations are available for this server?

Integrates with Electron CDP to capture screenshots and perform vision analysis on Electron applications. Uses local Ollama models for AI-powered vision analysis, including image description, OCR, and text detection. Allows analyzing screenshots from Selenium-driven browser automation using vision capabilities.

How do I use vision-mcp?

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 analyze screenshot.png for error messages" 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

by arealicehole

Overview Schema Related Servers Score Discussions

JavaScript

Local

Vision MCP Server 🔍

A powerful Model Context Protocol (MCP) server that provides AI-powered vision analysis capabilities using local Ollama models. Analyze screenshots, extract text, detect UI elements, and debug applications with state-of-the-art vision language models.

License: MIT Node.js Ollama

🚀 Features

🔍 Vision Analysis - Analyze screenshots and describe UI state, errors, or issues
📝 OCR Extraction - Extract text from images using VLM or Tesseract
🎯 Text Detection - Find specific text with bounding boxes for automation
🏥 Health Monitoring - Check Ollama connection and available models
🔌 Universal Integration - Works with Electron, Selenium, Playwright, and more
🏃‍♂️ CLI Tool - Standalone command-line interface for any workflow
⚡ High Performance - Optimized for 16GB+ VRAM with local models

Related MCP server: helix-pilot

📦 Installation

Prerequisites

Node.js 18+
Ollama (for vision models)
Claude Code (for MCP integration)
16GB+ VRAM recommended for optimal performance

Quick Setup

# 1. Clone the repository
git clone https://github.com/YOUR_USERNAME/vision-mcp.git
cd vision-mcp

# 2. Install dependencies
npm install

# 3. Install Ollama and models
curl -fsSL https://ollama.com/install.sh | sh
ollama serve &
ollama pull llava:7b

# 4. Run setup script
chmod +x setup.sh
./setup.sh

# 5. Test installation
./vlm.mjs health

Claude Code Integration

Add to your ~/.claude.json in the mcpServers section:

{
  "mcpServers": {
    "vision": {
      "type": "stdio",
      "command": "/home/ice/vision-mcp-wrapper.sh",
      "args": [],
      "env": {}
    }
  }
}

🎯 Quick Start

CLI Usage

# Health check
vlm health

# Analyze screenshot
vlm describe --image screenshot.png --prompt "What errors are visible?"

# Extract text (OCR)
vlm ocr --image document.png --engine tesseract

# Find UI elements
vlm find --image app.png --query "Submit button"

MCP Usage in Claude

Use vision.describe to analyze this screenshot for errors
Use vision.find_text to locate the "Login" button  
Use vision.ocr to extract all visible text
Use vision.health to check model status

🛠️ Available Tools

`vision.describe`

Analyze images and describe UI state, errors, or issues.

Parameters:

image_b64 (string): Base64-encoded image
prompt (string, optional): Custom analysis prompt
model (string, optional): Ollama model to use
max_tokens (number, optional): Maximum response tokens

Example:

{
  "image_b64": "iVBORw0KGgoAAAANSUhEU...",
  "prompt": "Identify any error messages or broken UI elements",
  "model": "llava:7b"
}

`vision.ocr`

Extract text from images using VLM or Tesseract.

Parameters:

image_b64 (string): Base64-encoded image
engine (string, optional): "vlm" or "tesseract"
model (string, optional): Model for VLM OCR
structured (boolean, optional): Return structured JSON

`vision.find_text`

Locate specific text and return bounding boxes.

Parameters:

image_b64 (string): Base64-encoded image
query (string): Text to search for
model (string, optional): Ollama model to use
fuzzy (boolean, optional): Allow fuzzy matching

`vision.health`

Check Ollama connection and available models.

🔗 Integration Examples

Electron CDP Integration

// Add to your Electron MCP server
server.registerTool(
  "browser_vision_check",
  {
    title: "Analyze page with AI vision",
    inputSchema: z.object({
      prompt: z.string().default("Check for errors"),
      fullPage: z.boolean().default(false)
    })
  },
  async ({ prompt, fullPage }) => {
    const page = await pickPage();
    const screenshot = await page.screenshot({ 
      type: "png", 
      encoding: "base64", 
      fullPage 
    });
    
    // Call Vision MCP via Claude
    return {
      content: [{
        type: "text",
        text: JSON.stringify({
          next_tool: "vision.describe",
          args: { image_b64: screenshot, prompt }
        })
      }]
    };
  }
);

Selenium Integration

// Shell out to vlm CLI from Selenium MCP
async function analyzeSeleniumPage(driver, prompt) {
  const screenshot = await driver.takeScreenshot();
  const tmpPath = `/tmp/selenium-${Date.now()}.png`;
  
  await fs.writeFile(tmpPath, Buffer.from(screenshot, "base64"));
  
  const { execFile } = require("child_process");
  return new Promise((resolve, reject) => {
    execFile("/home/ice/vision-mcp/vlm.mjs",
      ["describe", "--image", tmpPath, "--prompt", prompt],
      (error, stdout) => {
        fs.unlink(tmpPath); // Cleanup
        if (error) reject(error);
        else resolve(JSON.parse(stdout));
      }
    );
  });
}

Playwright in Docker

# docker-compose.yml
services:
  playwright:
    image: mcr.microsoft.com/playwright:v1.54.2-noble
    extra_hosts:
      - "host.docker.internal:host-gateway"
    environment:
      - OLLAMA_HOST=host.docker.internal

// Direct Ollama call from container
async function analyzeWithVision(page, prompt) {
  const screenshot = await page.screenshot({ encoding: "base64" });
  
  const response = await fetch("http://host.docker.internal:11434/api/generate", {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      model: "llava:7b",
      prompt: `Analyze this UI screenshot: ${prompt}`,
      images: [screenshot],
      stream: false
    })
  });
  
  const data = await response.json();
  return data.response;
}

🤖 Supported Models

Recommended Models by Use Case

Use Case	Model	Size	Strengths
General UI Analysis	`llava:7b` ✅	4.1GB	Reliable, fast, good reasoning
OCR & Text Extraction	`minicpm-v:8b-2.6`	~8GB	State-of-the-art OCR accuracy
Document Analysis	`qwen2.5vl:7b`	~8GB	Excellent for complex layouts
Lightweight	`llava:7b`	4.1GB	Best speed/accuracy balance

Model Installation

# Current default (installed)
ollama pull llava:7b

# Better OCR model
ollama pull minicpm-v:8b-2.6

# Best document analysis (when available)
ollama pull qwen2.5vl:7b

⚙️ Configuration

Environment Variables

Variable	Default	Description
`OLLAMA_HOST`	`127.0.0.1`	Ollama server host
`OLLAMA_PORT`	`11434`	Ollama server port
`VISION_MODEL`	`llava:7b`	Default vision model
`OCR_MODEL`	`llava:7b`	Default OCR model
`MAX_TOKENS`	`1024`	Maximum response tokens

Model Switching

# Use different model temporarily
VISION_MODEL=minicpm-v:8b-2.6 vlm describe --image screenshot.png

# Or specify in tool call
vlm describe --image screenshot.png --model minicpm-v:8b-2.6

🔧 Development

Project Structure

vision-mcp/
├── vision-mcp.mjs          # Main MCP server
├── vlm.mjs                 # CLI tool
├── package.json            # Dependencies
├── setup.sh               # Installation script
├── vision-mcp-wrapper.sh  # MCP wrapper
├── integration-examples.md # Integration guides
└── docs/                  # Additional documentation

Running Development Server

# Start Ollama
ollama serve &

# Test MCP server
node vision-mcp.mjs

# Test CLI tool
./vlm.mjs health

Adding New Models

Pull model: ollama pull model-name
Update defaults in vision-mcp.mjs and vlm.mjs
Test with: vlm describe --model model-name --image test.png

🐛 Troubleshooting

Common Issues

🔴 "Ollama not responding"

ollama serve &
curl http://localhost:11434/api/tags

🔴 "Model not found"

ollama list
ollama pull llava:7b

🔴 "Tesseract not found"

# Fedora/RHEL
sudo dnf install tesseract tesseract-langpack-eng

# Ubuntu/Debian  
sudo apt install tesseract-ocr

🔴 "Permission denied"

chmod +x vision-mcp.mjs vlm.mjs vision-mcp-wrapper.sh

Debug Mode

# Enable debug output
DEBUG=1 ./vlm.mjs describe --image test.png

# Check MCP server logs
journalctl --user -f | grep vision-mcp

Performance Optimization

# Keep models warm (optional)
curl http://localhost:11434/api/generate \
  -d '{"model":"llava:7b","prompt":"warmup","keep_alive":"10m"}'

# Monitor GPU usage
nvidia-smi -l 1

📊 Benchmarks

Performance on 16GB VRAM

Model	Memory Usage	Speed	Accuracy	Best For
`llava:7b`	~4GB	⚡⚡⚡ Fast	⭐⭐⭐ Good	General use, UI analysis
`minicpm-v:8b-2.6`	~8GB	⚡⚡ Medium	⭐⭐⭐⭐ Excellent	OCR, text extraction
`qwen2.5vl:7b`	~8GB	⚡⚡ Medium	⭐⭐⭐⭐ Excellent	Document analysis

🤝 Contributing

Fork the repository
Create a feature branch: git checkout -b feature-name
Commit changes: git commit -am 'Add feature'
Push to branch: git push origin feature-name
Submit a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Ollama - Local model runtime
LLaVA - Vision language model
MCP SDK - Protocol implementation
Claude Code - Development environment

Electron CDP MCP - Electron automation
Selenium MCP - Browser testing
Playwright MCP - Web automation

Made with ❤️ for the Claude Code ecosystem

🌟 Star this repo if you find it helpful! 🐛 Report issues on GitHub 💬 Join discussions in the community

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

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Vision MCP Server 🔍

🚀 Features

📦 Installation

Prerequisites

Quick Setup

Claude Code Integration

🎯 Quick Start

CLI Usage

MCP Usage in Claude

🛠️ Available Tools

vision.describe

vision.ocr

vision.find_text

vision.health

🔗 Integration Examples

Electron CDP Integration

Selenium Integration

Playwright in Docker

🤖 Supported Models

Recommended Models by Use Case

Model Installation

⚙️ Configuration

Environment Variables

Model Switching

🔧 Development

Project Structure

Running Development Server

Adding New Models

🐛 Troubleshooting

Common Issues

Debug Mode

Performance Optimization

📊 Benchmarks

Performance on 16GB VRAM

🤝 Contributing

📄 License

🙏 Acknowledgments

🔗 Related Projects

Maintenance

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API

`vision.describe`

`vision.ocr`

`vision.find_text`

`vision.health`