Debugg AI MCP

Debugg AI MCP

What is Debugg AI MCP about?

Debugg AI MCP delivers AI‑driven, zero‑configuration end‑to‑end (E2E) testing for any web application. It lets developers describe test scenarios in natural language, then automatically runs browser tests, captures console/network logs, takes screenshots, and reports results—all through the Model Context Protocol.

How to use Debugg AI MCP?

1. Create an API key – sign up at https://debugg.ai and generate a personal API key.
2. Run the server (recommended):

   npx -y @debugg-ai/debugg-ai-mcp
   

   or use the provided Docker image.
3. Call the MCP tools from any MCP‑compatible client (Claude Desktop, LangChain, etc.) using the defined tool names such as debugg_ai_test_page_changes or debugg_ai_start_live_session.
4. Integrate with CI/CD – the server exposes test results that can be visualized in the Debugg.AI dashboard.

Key Features of Debugg AI MCP

• Zero‑config E2E testing – write natural‑language prompts instead of code.
• Live session monitoring – real‑time console, network traffic, and screenshot streaming.
• Test suite management – create, list, and track both feature‑based and commit‑based suites.
• Progress streaming – step‑by‑step execution updates with visual snapshots.
• Universal client compatibility – works with any MCP‑compatible agent.
• CI/CD ready – results appear in the Debugg.AI dashboard for automated pipelines.
• Docker support – optional container‑based deployment.

Use Cases

• Automated regression testing – generate tests from git commits and run them on every push.
• Rapid QA for new features – QA engineers describe a workflow in plain English and instantly get a validated test.
• Debugging UI issues – start a live session, watch network calls and console logs while the AI interacts with the page.
• Continuous integration pipelines – embed the MCP server in GitHub Actions or other CI tools to automatically validate UI behavior.
• Documentation validation – ensure tutorial steps stay functional by turning them into AI‑generated tests.

FAQ

Q: Do I need to write any test code? A: No. Tests are generated from natural‑language prompts.

Q: Which browsers are supported? A: The server uses a headless Chrome/Chromium instance, compatible with any modern web app.

Q: Can I run the server locally? A: Yes. After installing dependencies (npm install), start with node dist/index.js or the npx command.

Q: Is Docker required? A: Docker is optional; it provides an isolated environment but the npx approach is the easiest.

Q: What environment variables are mandatory? A: DEBUGGAI_API_KEY must be set. Optional variables include DEBUGGAI_LOCAL_PORT, DEBUGGAI_LOCAL_REPO_NAME, and DEBUGGAI_LOCAL_REPO_PATH.

Q: How do I integrate with Claude Desktop? A: Add the MCP server configuration to the Claude Desktop settings file as shown in the README.

🧪 Official MCP Server for Debugg AI

AI-powered development and testing toolkit implementing the Model Context Protocol (MCP), designed to give AI agents comprehensive testing, debugging, and code analysis capabilities.

Transform your development workflow with:

• Zero-config E2E testing - Run browser tests with natural language descriptions
• Live session monitoring - Real-time browser console, network, and screenshot monitoring
• Test suite management - Create and manage comprehensive test suites
• Seamless CI/CD integration - View all test results in your Debugg.AI App dashboard

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🚀 Features

12 Focused Development Tools

• 🧪 E2E Testing Suite - Run browser tests, create test suites, and generate commit-based tests
• 🖥️ Live Session Monitoring - Real-time browser console, network traffic, and screenshot monitoring
• 📊 Test Management - List, create, and track test suites and commit-based test suites
• 📱 Real-time Progress - Live updates with screenshots and step-by-step execution
• 🌐 Universal Compatibility - Works with any MCP-compatible client (Claude Desktop, LangChain, etc.)

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Examples

Input prompt: "Test the ability to create an account and login"

Results:

**Task Completed**

- Duration: 86.80 seconds
- Final Result: Successfully completed the task of signing up and logging into the account with the email 'alice.wonderland1234@example.com'.
- Status: Success

Full Demo:

Watch a more in-depth, Full Use Case Demo

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🛠️ Quick Setup

1. Get Your API Key

Create a free account at debugg.ai and generate your API key.

2. Choose Your Installation Method

Option A: NPX (Recommended)

npx -y @debugg-ai/debugg-ai-mcp

Option B: Docker

docker run -i --rm --init \
  -e DEBUGGAI_API_KEY=your_api_key \
  quinnosha/debugg-ai-mcp

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🧰 Available Tools

E2E Testing Tools

• debugg_ai_test_page_changes - Run browser tests with natural language descriptions
• debugg_ai_create_test_suite - Create organized test suites for features
• debugg_ai_create_commit_suite - Generate tests based on git commits
• debugg_ai_get_test_status - Monitor test execution and results

Test Management Tools

• debugg_ai_list_tests - List all E2E tests with filtering and pagination
• debugg_ai_list_test_suites - List all test suites with filtering options
• debugg_ai_list_commit_suites - List all commit-based test suites

Live Session Monitoring Tools

• debugg_ai_start_live_session - Start a live browser session with real-time monitoring
• debugg_ai_stop_live_session - Stop an active live session
• debugg_ai_get_live_session_status - Get the current status of a live session
• debugg_ai_get_live_session_logs - Retrieve console and network logs from a live session
• debugg_ai_get_live_session_screenshot - Capture screenshots from an active live session

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⚙️ Configuration

For Claude Desktop

Add this to your MCP settings file:

{
  "mcpServers": {
    "debugg-ai-mcp": {
      "command": "npx",
      "args": ["-y", "@debugg-ai/debugg-ai-mcp"],
      "env": {
        "DEBUGGAI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Optional Environment Variables

# Required
DEBUGGAI_API_KEY=your_api_key

# Optional (with sensible defaults)
DEBUGGAI_LOCAL_PORT=3000                    # Your app's port
DEBUGGAI_LOCAL_REPO_NAME=your-org/repo      # GitHub repo name
DEBUGGAI_LOCAL_REPO_PATH=/path/to/project   # Project directory

💡 Usage Examples

Run a Quick E2E Test

"Test the user login flow on my app running on port 3000"

Analyze Your Project

"What frameworks and languages are used in my codebase?"

Get Issue Insights

"Show me all high-priority issues in my project"

Generate Test Coverage

"Generate test coverage for the authentication module"

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🧑‍💻 Local Development

# Install dependencies
npm install

# Run tests
npm test

# Build project
npm run build

# Start server locally
node dist/index.js

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📁 Project Structure

debugg-ai-mcp/
├── config/          # Configuration management  
├── tools/           # 14 MCP tool definitions
├── handlers/        # Tool implementation logic
├── services/        # DebuggAI API integration
├── utils/           # Shared utilities & logging
├── types/           # TypeScript type definitions
├── __tests__/       # Comprehensive test suite
└── index.ts         # Main server entry point

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🚀 Publishing & Releases

This project uses automated publishing to NPM. Here's how it works:

Automatic Publishing

• Every push to main triggers automatic NPM publishing
• Only publishes if the version doesn't already exist
• Includes full test suite validation and build verification

Version Management

# Bump version locally
npm run version:patch  # 1.0.15 → 1.0.16
npm run version:minor  # 1.0.15 → 1.1.0
npm run version:major  # 1.0.15 → 2.0.0

# Check package contents
npm run publish:check

Manual Version Bump via GitHub

1. Go to Actions → Version Bump
2. Click "Run workflow"
3. Select version type or enter custom version
4. Workflow will update version and trigger publish

Setup for Contributors

See .github/PUBLISHING_SETUP.md for complete setup instructions.

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💬 Support & Links

• 📖 Documentation: debugg.ai/docs
• 🐛 Issues: GitHub Issues
• 💬 Discord: Join our community
• 🌐 Dashboard: app.debugg.ai

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🔒 License

Apache-2.0 License © 2025 DebuggAI

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