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Vuda (visual Ui Debug Agent) MCP Server

@samihalawa

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

samihalawa

提交者

samihalawa

关于 Vuda (visual Ui Debug Agent) MCP Server

暂无概览

#vuda#visual-debugging#ui-testing

Vuda (visual Ui Debug Agent) MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "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 工具

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概览

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

常见问题

1

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.

2

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.

3

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.

4

Does VUDA support CI/CD integration?

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

5

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.

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