Best for
- Development teams wanting to automate end-to-end testing of web applications
- CI pipelines that need quick page sanity checks
- Users of the DebuggAI platform who want MCP integration
D
Debugg AI MCP Server
Community
Zero-Config, Fully AI-Managed End-to-End Testing
GitHub source repository ↗
58FMRS · C
The Debugg AI MCP server provides a comprehensive AI-driven browser testing suite, suitable for teams needing automated end-to-end tests with integrated test management.
The Debugg AI MCP server provides AI-powered browser testing via the Model Context Protocol. Point it at any URL (or localhost) and describe what to test — an AI agent will browse your app and return pass/fail with screenshots. The server offers 8 tools across browser testing, project management, environment setup, test suite and case management, and execution tracking. It supports multi-entity operations (projects, environments, test suites, test cases, executions) and includes a lightweight page probe for no-LLM smoke tests. Requires Node.js 20.20.0 or later and an API key from debugg.ai.
Tools
check_app_in_browser
Runs an AI browser agent to test your app based on a natural language description, with automatic tunneling for localhost URLs.
trigger_crawl
Triggers a server-side browser-agent crawl to populate the project's knowledge graph.
probe_page
Lightweight no-LLM batch page probe that checks 1-20 URLs for rendered state, console errors, and network summary.
project
Manage projects: get, list, create.
environment
Manage environments and credentials, including login session operations.
test_suite
Manage test suites: list, create, run, get results, delete.
test_case
Manage test cases: create, update, delete.
executions
Retrieve execution details and artifacts.
Setup
- Get an API key at debugg.ai. 2. Add the server to your MCP client config using npx and set the DEBUGGAI_API_KEY environment variable. 3. Alternatively, use Docker with the provided image (note: the current image may lack support for localhost tools). 4. For remote HTTP transport, set DEBUGGAI_MCP_TRANSPORT=http and other optional variables.
claude_desktop_config.json
{
"mcpServers": {
"debugg-ai": {
"command": "npx",
"args": ["-y", "@debugg-ai/debugg-ai-mcp"],
"env": {
"DEBUGGAI_API_KEY": "your_api_key_here"
}
}
}
}
Fit and risk
Not for
- Unit tests or integration tests that don't require AI
- Offline environments that cannot access the service (requires API key)
- Fully local or self-hosted tests that don't rely on external services
Required permissions
- Requires internet access to the DebuggAI backend and an API key
- Requires the ability to allow an AI agent to perform browser interactions
- May require tunneling for localhost access (via ngrok/caddy)
Risks and side effects
- AI agent behavior may affect production if run against production
- Testing depends on external service and API key, potential availability issues
- Credential handling: passwords are write-only, but environment variables must be stored securely
Troubleshooting
- Ensure DEBUGGAI_API_KEY is set and valid
- Check Node.js version (20.20.0+)
- For localhost URLs, ensure the caddy binary is available (or set via CADDY_BIN)
- Verify that the local service is running if necessary
Use cases
End-to-end AI-driven testing of web apps based on natural language descriptions
Lightweight page-based smoke tests after each pull request without LLM overhead
Managing test suites, cases, and environments for continuous testing
Debugging runtime issues with HAR and console log capture
Supported clients
Claude DesktopFull support
Claude CodeFull support