Vuda (visual Ui Debug Agent) MCP Server

@samihalawa

Basic information

About Vuda (visual Ui Debug Agent) MCP Server

No overview available yet

#vuda#visual-debugging#ui-testing

Vuda (visual Ui Debug Agent) MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "github": {
      "name": "VUDA (Visual UI Debug Agent)",
      "url": "https://github.com/samihalawa/visual-ui-debug-agent-mcp",
      "innovation": true,
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "GITHUB_PERSONAL_ACCESS_TOKEN=YOUR_GITHUB_TOKEN",
        "samihalawa/visual-ui-debug-agent-mcp"
      ],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "YOUR_GITHUB_TOKEN"
      }
    }
  }
}

Vuda (visual Ui Debug Agent) MCP Server tools

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Overview

What is Vuda (visual Ui Debug Agent)?

VUDA is an autonomous debugging agent that empowers AI models to visually analyze, test, and debug web interfaces through Playwright. It 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 without human intervention.

How to use Vuda (visual Ui Debug Agent)?

Install via an MCP gateway, the quick installation script, npm (npm install -g visual-ui-debug-agent-mcp), Docker (docker pull luigi1234/visual-ui-debug-agent:latest), or Smithery. After installation, start the server with the command vuda or visual-ui-debug-agent. Use the provided MCP tools (e.g., enhanced_page_analyzer, ui_workflow_validator) by calling them through your MCP client.

Key features of Vuda (visual Ui Debug Agent)

Performs comprehensive visual analysis of web pages
Detects UI issues by inspecting visual elements and properties
Automatically tests common user workflows without manual scripts
Validates API endpoints and verifies backend responses
Tracks visual changes between application versions
Monitors console logs for errors and warnings
Analyzes performance metrics to identify bottlenecks
Converts visual information into structured data for non‑vision models

Use cases of Vuda (visual Ui Debug Agent)

Visual regression testing: compare page states to catch unexpected visual changes
End‑to‑end user flow validation: automate login, navigation, and feature tests
Performance optimization: measure load metrics and identify slow elements
Automated UI debugging: find and report layout or functionality issues

Frequently asked questions

What problem does VUDA solve?

It allows any AI model to visually debug web interfaces by converting visual information into structured data, enabling analysis without the model needing built‑in vision.

What installation methods are available?

VUDA can be installed via MCP gateway, a quick installation script, npm (global or per‑platform packages), Docker Hub, or Smithery integration.

How does it help non‑vision AI models?

It provides structured data about interactive elements (tag name, text, bounds, visibility) so models can understand UI structure without processing images.

Does VUDA support CI/CD integration?

Yes, it includes GitHub Actions workflows for building, testing, publishing to npm and Docker, and deploying to Smithery.

What runtime dependencies are required?

VUDA uses Playwright under the hood; it requires Node.js and the Playwright browser binaries. No specific runtime restrictions are mentioned in the README.

Comments

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