🧪 Official MCP Server for Debugg AI

Name: 🧪 Official MCP Server for Debugg AI
Author: debugg-ai

@debugg-ai

Repository Homepage

About 🧪 Official MCP Server for Debugg AI

Zero-Config, Fully AI-Managed End-to-End Testing for all code gen platforms.

#automation #browser #web #end-to-end-testing

Basic information

Config

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

{
  "mcpServers": {
    "debugg-ai-mcp": {
      "command": "node",
      "args": [
        "dist/index.js"
      ],
      "env": {
        "DEBUGGAI_API_KEY": "your key here",
        "TEST_USERNAME_EMAIL": "test email here",
        "TEST_USER_PASSWORD": "test password here",
        "MCP_REQUEST_TIMEOUT_RESET_ON_PROGRESS": "true",
        "DEBUGGAI_LOCAL_PORT": 3000,
        "DEBUGGAI_LOCAL_REPO_NAME": "your repo name here",
        "DEBUGGAI_LOCAL_BRANCH_NAME": "your branch name here",
        "DEBUGGAI_LOCAL_REPO_PATH": "/Users/your username here/Documents/GitHub/your repo name here",
        "DEBUGGAI_LOCAL_FILE_PATH": "optional file path here"
      },
      "options": {}
    },
    "debugg-ai-mcp-live": {
      "command": "npx",
      "args": [
        "-y",
        "@debugg-ai/debugg-ai-mcp"
      ],
      "env": {
        "DEBUGGAI_API_KEY": "your key here",
        "TEST_USERNAME_EMAIL": "test email here",
        "TEST_USER_PASSWORD": "test password here",
        "MCP_REQUEST_TIMEOUT_RESET_ON_PROGRESS": "true",
        "DEBUGGAI_LOCAL_PORT": 3000,
        "DEBUGGAI_LOCAL_REPO_NAME": "your repo name here",
        "DEBUGGAI_LOCAL_BRANCH_NAME": "your branch name here",
        "DEBUGGAI_LOCAL_REPO_PATH": "/Users/your username here/Documents/GitHub/your repo name here",
        "DEBUGGAI_LOCAL_FILE_PATH": "optional file path here"
      },
      "options": {}
    },
    "debugg-ai-mcp-docker-live": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--init",
        "-e",
        "DEBUGGAI_API_KEY=your key here",
        "-e",
        "DEBUGGAI_LOCAL_PORT=3000",
        "-e",
        "DEBUGGAI_LOCAL_REPO_NAME=your repo name here",
        "-e",
        "DEBUGGAI_LOCAL_BRANCH_NAME=your branch name here",
        "-e",
        "DEBUGGAI_LOCAL_REPO_PATH=/Users/your username here/Documents/GitHub/your repo name here",
        "quinnosha/debugg-ai-mcp"
      ],
      "env": {
        "DEBUGGAI_API_KEY": "your key here",
        "TEST_USERNAME_EMAIL": "test email here",
        "TEST_USER_PASSWORD": "test password here",
        "MCP_REQUEST_TIMEOUT_RESET_ON_PROGRESS": "true",
        "DEBUGGAI_LOCAL_PORT": 3000,
        "DEBUGGAI_LOCAL_REPO_NAME": "your repo name here",
        "DEBUGGAI_LOCAL_BRANCH_NAME": "your branch name here",
        "DEBUGGAI_LOCAL_REPO_PATH": "/Users/your username here/Documents/GitHub/your repo name here",
        "DEBUGGAI_LOCAL_FILE_PATH": "optional file path here"
      }
    }
  }
}

Tools

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Overview

What is 🧪 Official MCP Server for Debugg AI?

The 🧪 Official MCP Server for Debugg AI is an MCP server that provides AI-powered browser testing. Point it at any URL (including localhost), describe what to test in natural language, and an AI agent browses the application, returning pass/fail results with screenshots. It is intended for developers and QA teams who want automated, AI-driven browser testing through MCP clients.

How to use 🧪 Official MCP Server for Debugg AI?

Requires Node.js 20.20.0 or later and an API key from debugg.ai. Add the server configuration to your MCP client’s settings, using npx -y @debugg-ai/debugg-ai-mcp and the environment variable DEBUGGAI_API_KEY. Alternatively, run with Docker: docker run -i --rm --init -e DEBUGGAI_API_KEY=your_api_key quinnosha/debugg-ai-mcp. After setup, invoke tools like check_app_in_browser with natural language instructions.

Key features of 🧪 Official MCP Server for Debugg AI

Eight tools: three Browser tools and five action-based tools.
AI agent (check_app_in_browser) navigates, interacts, and returns screenshots.
Lightweight probe_page tool (no LLM) for batch URL checks.
trigger_crawl populates a project’s knowledge graph.
Action-based tools for project, environment, test suite, test case, and execution management.
Auto-tunneling of localhost URLs via ngrok.
Returns HAR (network trace) and console logs for each browser session.
Read-only resources via debugg-ai:// URIs.

Use cases of 🧪 Official MCP Server for Debugg AI

Automated end‑to‑end testing of web applications from an MCP client.
Quick smoke tests across multiple routes after a refactor using probe_page.
Detecting refetch loops and hydration errors via captured HAR and console logs.
Pre‑populating a project’s knowledge graph with trigger_crawl.
Running suites of test cases defined in the Debugg AI platform.

FAQ from 🧪 Official MCP Server for Debugg AI

What is the difference between `check_app_in_browser` and `probe_page`?

check_app_in_browser uses an AI agent (LLM) that navigates and interacts with the page, returning a pass/fail verdict with screenshots. probe_page is a lightweight, no‑LLM batch probe that captures rendered state (screenshot, console errors, network summary) for up to 20 URLs without scenario assertions.

How is authentication handled?

The server requires a DEBUGGAI_API_KEY environment variable set to an API key obtained from debugg.ai. The key is never exposed in tool responses. Missing keys surface as a structured tool error on the first invocation.

What runtime dependencies are needed?

The server requires Node.js version 20.20.0 or later (transitive requirement from posthog-node). It runs as a stdio MCP server, invoked via npx or Docker.

Where are browser session artifacts (HAR, console logs) stored?

Artifacts are uploaded to AWS S3 as presigned URLs. The URLs are short‑lived; they can be renewed by refetching the parent execution via the executions tool with action get and the execution UUID. Sensitive headers (Authorization, Cookie, token/secret/api_key) are scrubbed server‑side before persistence.

What are the known limits of the AI agent?

check_app_in_browser has an internal budget of approximately 25 steps per call. For broader test scenarios, split them across multiple calls. The probe_page tool accepts 1–20 URLs with a total performance budget of under 10

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