mcp-vision

MCP server for vision capabilities - screenshot and camera analysis using Ollama vision models.

Features

• Screenshot Analysis: Capture and analyze screenshots with AI
• Camera Capture: Take photos from webcam and analyze them
• Image Analysis: Analyze existing image files
• Streaming Output: Real-time streaming of AI analysis
• Multiple Models: Support for various vision models (llava, bakllava, etc.)

Installation

cd /Users/bard/Code/mcp-vision
npm install

Prerequisites

• Ollama must be running with a vision model installed:

  ollama pull llava
  

• macOS (for screenshot functionality)
• Camera access (for webcam features)

Tools

vision_screenshot

Take a screenshot and analyze it with AI.

{
  prompt: "What application is open?",  // optional
  model: "llava",                       // optional
  region: {                             // optional
    x: 100,
    y: 100,
    width: 500,
    height: 400
  }
}

vision_camera

Capture from camera and analyze.

{
  prompt: "What do you see?",  // optional
  model: "llava",              // optional
  device: "FaceTime HD Camera" // optional
}

vision_analyze_image

Analyze an existing image file.

{
  path: "/path/to/image.jpg",
  prompt: "Describe this image",  // optional
  model: "llava"                  // optional
}

vision_list_cameras

List available camera devices.

Usage with Claude Desktop

Add to your Claude Desktop configuration:

{
  "mcpServers": {
    "vision": {
      "command": "node",
      "args": ["/Users/bard/Code/mcp-vision/src/index.js"]
    }
  }
}

Integration with ELVIS

This tool can be integrated with ELVIS for enhanced visual context:

1. Use vision_screenshot to capture current screen state
2. Pass the analysis to elvis_delegate for context-aware task processing
3. ELVIS can use visual information to better understand and complete tasks

Example Workflow

// 1. Analyze what's on screen
vision_screenshot({ prompt: "What code is visible?" })

// 2. Use with ELVIS
elvis_delegate({ 
  task: "Fix the syntax error shown",
  context: "Based on the screenshot analysis"
})

Streaming Output

The tool streams AI responses in real-time, providing immediate feedback as the model analyzes images. This is shown in the MCP server logs and can be used for progress tracking.