What is Debugg AI?

Debugg AI is a testing platform that enables zero-configuration end-to-end (E2E) testing of web applications. It leverages the Model Context Protocol (MCP) to allow AI agents to test UI changes, simulate user behavior, and analyze visual outputs of web applications via natural language commands and CLI tools.

How to use Debugg AI?

To use Debugg AI, you can either:

1. Run it locally via NPX with minimal configuration:

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

2. Use Docker for more controlled test environments:

docker run -i --rm --init \
  -e DEBUGGAI_API_KEY=your_api_key \
  -e TEST_USERNAME_EMAIL=your_test_email \
  ...

3. Integrate it with MCP-compatible clients like Claude Desktop by configuring it in your MCP server list.

Key features of Debugg AI?

• MCP Protocol Support: Full implementation with CLI and tool registry.
• End-to-End Test Automation: Test UI features via natural language descriptions.
• Localhost Integration: Test running apps on any localhost port.
• Real-Time Notifications: Progress updates via MCP tool notifications.
• Screenshot Support: Capture final UI state for analysis.
• Stdio Server Compatibility: Works with any MCP-compatible client.

Use cases of Debugg AI?

1. Automating UI tests for account creation and login flows.
2. Simulating complex user journeys for feature validation.
3. Integrating automated testing into CI/CD pipelines.
4. Generating visual regression test reports.
5. Validating changes in development environments before production deployment.

FAQ from Debugg AI?

• What is the Model Context Protocol (MCP)?

MCP is a protocol for AI agents to interact with services, tools, and models in a standardized way.

• Is Debugg AI open-source?

The MCP server implementation is open-source, but usage of Debugg AI's platform requires an API key.

• Can I use Debugg AI without AI agents?

Yes, though designed for AI integration, it can also be used via CLI tools directly.

• How does pricing work?

Debugg AI offers a free tier with usage limits, and paid plans for higher volume testing.

🧪 Official MCP Server for Debugg AI

AI-driven browser automation and E2E test server implementing the Model Context Protocol (MCP), designed to help AI agents test UI changes, simulate user behavior, and analyze visual outputs of running web applications — all via natural language and CLI tools.

End to end testing used to be a nightmare. Not just to setup, but to manage over time as you made changes to your app.

Debugg AI's MCP server offers a NEW way to test, where you never have to worry about setting up playwright, local browsers or proxies with it fully remote, managed browsers that simply connect to a server running locally or remotely via a secure tunnel.

That means no distracting chrome pop ups as it's running tests, no managing chrome or playwright versions, and best of all - ZERO CONFIGURATION. Just grab an API key and add us to your MCP server list.

Should you want to later rerun those tests or create a suite of them to run in your CI / CD pipeline, you can see all historical test results in your dashboard - Debugg.AI App

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🚀 Features

• 🧠 MCP Protocol Support Full MCP server implementation with CLI and tool registry support.

• 🧪 End-to-End Test Automation Trigger UI tests based on user stories or natural language descriptions via the debugg_ai_test_page_changes tool.

• 🌐 Localhost Web App Integration Test your running dev app on any localhost port with simulated user flows.

• 🧾 MCP Tool Notifications Sends real-time progress updates back to clients with step descriptions and UI state goals.

• 🧷 Screenshot Support Capture final visual state of the page for LLMs with image rendering support.

• 🧱 Stdio Server Compatible Plug into any MCP-compatible client (like Claude Desktop, LangChain agents, etc.) via stdin/stdout.

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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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🛠️ Quickstart

Ensure you have created a free account and generated an API Key - DebuggAI

Option 1: NPX (Local Development)

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

Use this when testing or integrating into tools like Claude Desktop or your own AI agent.

Option 2: Docker

docker run -i --rm --init \
  -e DEBUGGAI_API_KEY=your_api_key \
  -e TEST_USERNAME_EMAIL=your_test_email \
  -e TEST_USER_PASSWORD=your_password \
  -e DEBUGGAI_LOCAL_PORT=3000 \
  -e DEBUGGAI_LOCAL_REPO_NAME=your-org/your-repo \
  -e DEBUGGAI_LOCAL_BRANCH_NAME=main \
  -e DEBUGGAI_LOCAL_REPO_PATH=/app \
  -e DEBUGGAI_LOCAL_FILE_PATH=/app/index.ts \
  quinnosha/debugg-ai-mcp

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🧰 MCP Tool: debugg_ai_test_page_changes

Description

Run an end-to-end test on a running web app, testing a UI feature or flow described in natural language. Allows AI agents in ANY code gen platform to quickly evaluate proposed changes and ensure new functionality works as expected.

Input Parameters

Name	Type	Required	Description
description	string	✅	What feature or page to test (e.g. "Signup page form")
localPort	number	❌	Port of your running app (default: 3000)
repoName	string	❌	GitHub repo name
branchName	string	❌	Current branch
repoPath	string	❌	Absolute path to the repo
filePath	string	❌	File to test

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🧪 Example Claude Desktop Config

{
  "mcpServers": {
    "debugg-ai-mcp": {
      "command": "npx",
      "args": ["-y", "@debugg-ai/debugg-ai-mcp"],
      "env": {
        "DEBUGGAI_API_KEY": "YOUR_API_KEY",
        "TEST_USERNAME_EMAIL": "test@example.com",
        "TEST_USER_PASSWORD": "supersecure",
        "DEBUGGAI_LOCAL_PORT": 3000,
        "DEBUGGAI_LOCAL_REPO_NAME": "org/project",
        "DEBUGGAI_LOCAL_BRANCH_NAME": "main",
        "DEBUGGAI_LOCAL_REPO_PATH": "/Users/you/project",
        "DEBUGGAI_LOCAL_FILE_PATH": "/Users/you/project/index.ts"
      }
    }
  }
}

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🔐 Environment Variables

Variable	Description	Required
DEBUGGAI_API_KEY	API key for calling DebuggAI backend	✅
TEST_USERNAME_EMAIL	Email of test user account	❌
TEST_USER_PASSWORD	Password of test user account	❌
DEBUGGAI_LOCAL_PORT	Local port your app runs on	✅
DEBUGGAI_LOCAL_REPO_NAME	GitHub repo name	❌
DEBUGGAI_LOCAL_BRANCH_NAME	Branch name	❌
DEBUGGAI_LOCAL_REPO_PATH	Local path to repo root	❌
DEBUGGAI_LOCAL_FILE_PATH	File to test	❌

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🧑‍💻 Local Development

# Clone the repo and install dependencies
npm install

# Copy the test config and insert your creds
cp test-config-example.json test-config.json

# Run the local node-built dist
npx @modelcontextprotocol/inspector --config test-config.json --server debugg-ai-mcp-node

# OR Run the MCP server locally from above toplevel dir. 
npx @modelcontextprotocol/inspector --config debugg-ai-mcp/test-config.json --server debugg-ai-mcp

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📁 Repo Structure

.
├── e2e-agents/             # E2E browser test runners
├── services/               # Client for DebuggAI API
├── tunnels /               # Secure connections to remote web browsers
├── index.ts                # Main MCP server entry
├── Dockerfile              # Docker build config
└── README.md

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🧱 Built With

• Model Context Protocol SDK

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💬 Feedback & Issues

For bugs, ideas, or integration help, open an issue or contact the DebuggAI team directly.

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🔒 License

MIT License © 2025 DebuggAI

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Made with 🩸, 💦, and 😭 in San Francisco