AI Vision MCP Server

Visual UI Debug Agent MCP

An autonomous debugging MCP server that empowers AI models to analyze, debug, and interact with web interfaces through Playwright. This server enables any AI model (even those without built-in vision capabilities) to visually inspect web pages, find UI bugs, test user workflows, and validate application performance - all without human intervention.

UI Automation Screenshot

Autonomous UI Debugging Agent

This MCP server functions as an AI-powered autonomous debugging agent that can:

Perform comprehensive visual analysis of web applications
Detect UI issues by inspecting visual elements and their properties
Automatically test common user workflows without manual test script creation
Validate API endpoints and verify backend responses
Track visual changes between application versions
Monitor console logs for errors and warnings
Analyze performance metrics to identify bottlenecks
Generate detailed reports with screenshots and recommendations

The server is designed to work intelligently, reusing browser sessions, avoiding unnecessary file creation, and focusing on the most important aspects of your application.

Installation Options

Using an MCP Gateway (Recommended)

The easiest way to install this MCP server is through any MCP-compatible gateway:

# Example with Claude gateway
claude-gateway install visual-ui-debug-agent-mcp

Quick Installation Script

Use our one-line installation script:

curl -s https://raw.githubusercontent.com/samihalawa/visual-ui-debug-agent-mcp/main/scripts/install-global.sh | bash

NPM Installation

For global installation via npm:

# Install globally
npm install -g visual-ui-debug-agent-mcp

# Start the server
visual-ui-debug-agent-mcp

Docker Hub Installation

For containerized deployment:

# Pull the image from Docker Hub
docker pull samihalawa/visual-ui-debug-agent-mcp:latest

# Run the container
docker run -p 8080:8080 samihalawa/visual-ui-debug-agent-mcp:latest

Smithery Integration

This package is fully Smithery-compatible using the included configuration file:

# Install with Smithery
smithery install visual-ui-debug-agent-mcp

# Or run with your API key
npm run smithery:key YOUR_SMITHERY_API_KEY

For full installation and usage instructions, see the Smithery Integration Guide.

Complete Tool Reference

Primary Visual Analysis Tools

1. `enhanced_page_analyzer` 🔍

Provides comprehensive analysis of web pages with interactive elements mapping, performance metrics, and visual inspection.

const analysis = await mcp.callTool("enhanced_page_analyzer", {
  url: "https://example.com/dashboard",
  includeConsole: true,
  mapElements: true,
  fullPage: true
});

2. `ui_workflow_validator` 🔄

Automatically tests full user journeys by executing and validating a sequence of UI interactions.

const result = await mcp.callTool("ui_workflow_validator", {
  startUrl: "https://example.com/login",
  taskDescription: "User login flow",
  steps: [
    { description: "Enter username", action: "fill", selector: "#username", value: "test" },
    { description: "Enter password", action: "fill", selector: "#password", value: "pass" },
    { description: "Click login", action: "click", selector: "button[type='submit']" },
    { description: "Verify dashboard loads", action: "verifyElementVisible", selector: ".dashboard" }
  ],
  captureScreenshots: "all"
});

3. `visual_comparison` 👁️

Compares two web pages or UI states to identify visual differences.

const diff = await mcp.callTool("visual_comparison", {
  url1: "https://example.com/before",
  url2: "https://example.com/after",
  threshold: 0.05
});

4. `screenshot_url` 📸

Captures high-quality screenshots of any URL with options for full page or specific elements.

const screenshot = await mcp.callTool("screenshot_url", {
  url: "https://example.com/profile",
  fullPage: true,
  device: "iPhone 13"
});

5. `batch_screenshot_urls` 📷

Takes screenshots of multiple URLs in a single operation for efficient comparison.

const screenshots = await mcp.callTool("batch_screenshot_urls", {
  urls: ["https://example.com/page1", "https://example.com/page2"],
  fullPage: true
});

User Flow Testing Tools

6. `navigation_flow_validator` 🧭

Tests multi-step navigation sequences with validation.

const navResult = await mcp.callTool("navigation_flow_validator", {
  startUrl: "https://example.com",
  steps: [
    { action: "click", selector: "a.products" },
    { action: "wait", waitTime: 1000 },
    { action: "click", selector: ".product-item" }
  ],
  captureScreenshots: true
});

7. `api_endpoint_tester` 🔌

Tests multiple API endpoints and verifies responses for backend validation.

const apiTest = await mcp.callTool("api_endpoint_tester", {
  url: "https://api.example.com/v1",
  endpoints: [
    { path: "/users", method: "GET" },
    { path: "/products", method: "GET" }
  ],
  authToken: "Bearer token123"
});

DOM and Performance Analysis

8. `dom_inspector` 🔬

Inspects DOM elements and their properties in detail.

const elementInfo = await mcp.callTool("dom_inspector", {
  url: "https://example.com",
  selector: "nav.main-menu",
  includeChildren: true,
  includeStyles: true
});

9. `console_monitor` 📟

Monitors and captures console logs for error detection.

const logs = await mcp.callTool("console_monitor", {
  url: "https://example.com/app",
  filterTypes: ["error", "warning"],
  duration: 5000
});

10. `performance_analysis` ⚡

Measures and analyzes page load performance metrics.

const perfMetrics = await mcp.callTool("performance_analysis", {
  url: "https://example.com/dashboard",
  iterations: 3
});

Low-Level Playwright Controls

11. `screenshot_local_files` 📁

Takes screenshots of local HTML files.

const localScreenshot = await mcp.callTool("screenshot_local_files", {
  filePath: "/path/to/local/file.html"
});

12. Direct Playwright Actions

Complete set of low-level Playwright controls for precise automation:

playwright_navigate: Navigate to specific URLs
playwright_click: Click on elements
playwright_iframe_click: Click elements inside iframes
playwright_fill: Fill form fields
playwright_select: Select dropdown options
playwright_hover: Hover over elements
playwright_evaluate: Run JavaScript in the page context
playwright_console_logs: Get console logs
playwright_get_visible_text: Extract visible text
playwright_get_visible_html: Get visible HTML
playwright_go_back: Navigate back
playwright_go_forward: Navigate forward
playwright_press_key: Press keyboard keys
playwright_drag: Drag and drop elements
playwright_screenshot: Take custom screenshots

Autonomous Debugging Workflows

The MCP server can autonomously perform complete debugging workflows by combining tools. For example:

Visual Regression Testing

// 1. Analyze the current version
const currentAnalysis = await mcp.callTool("enhanced_page_analyzer", {...});

// 2. Compare with previous version
const comparisonResult = await mcp.callTool("visual_comparison", {...});

// 3. Generate visual difference report
const report = await mcp.callTool("ui_workflow_validator", {...});

End-to-End User Flow Validation

// 1. Start with login flow
const loginResult = await mcp.callTool("ui_workflow_validator", {...});

// 2. Validate core features
const featureResults = await mcp.callTool("navigation_flow_validator", {...});

// 3. Test API endpoints
const apiResults = await mcp.callTool("api_endpoint_tester", {...});

Performance Optimization

// 1. Analyze initial performance
const initialPerformance = await mcp.callTool("performance_analysis", {...});

// 2. Identify slow-loading elements
const elementPerformance = await mcp.callTool("dom_inspector", {...});

// 3. Monitor console for errors
const consoleErrors = await mcp.callTool("console_monitor", {...});

Visual Analysis Examples

Element Mapping

The MCP server automatically maps all interactive elements on a page, making it easy for an AI model to understand the UI structure.

Visual Comparison

The visual comparison tool highlights differences between UI states, perfect for catching unexpected visual changes.

Integration Options

Integration with Smithery

# smithery.yaml configuration
startCommand:
  type: stdio
  configSchema:
    type: object
    properties:
      port:
        type: number
        description: Port number for the MCP server
      debug:
        type: boolean
        description: Enable debug mode

Integration with GLAMA

// glama.json configuration
{
  "name": "visual-ui-debug-agent-mcp",
  "version": "1.0.2",
  "settings": {
    "port": 8080,
    "headless": true,
    "maxConcurrentSessions": 5
  }
}

Integration with Non-Vision Models

The MCP server converts visual information into structured data that can be used by any AI model, even those without vision capabilities:

// The model receives structured data about visual elements
{
  "interactiveElements": [
    {
      "tagName": "button",
      "text": "Submit",
      "bounds": {"x": 120, "y": 240, "width": 100, "height": 40},
      "visible": true
    },
    // More elements...
  ]
}

CI/CD Integration

This MCP server includes GitHub Actions workflows for continuous integration and deployment:

Build and Test: Validates code quality
NPM Publishing: Automates package publishing
Docker Publishing: Creates and pushes Docker images
Smithery Publishing: Deploys to Smithery platform

License

This project is licensed under the ISC License.

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Provides AI-powered visual analysis capabilities for Claude and other MCP-compatible AI assistants, allowing them to capture and analyze screenshots, perform file operations, and generate UI/UX reports.

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