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Official MCP Server · Catch bugs before your users

The MCP AI Server That Gives Your Agents Eyes In The Browser

Let your AI coding assistant test its own work in a real browser and catch bugs before your users do.

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The only MCP server that tests the version on your machine, keeps your passwords safe, and manages your whole project. Point it at http://localhost:3000 or any live URL.

npx -y @debugg-ai/debugg-ai-mcp · works with Claude Desktop, Claude Code, Cursor, and any MCP client

Why developers pick this MCP AI server

Most browser MCPs are thin wrappers around a driver. This is the whole testing platform, built for AI coding agents.

Test the version on your machine

Point the agent at the app running on your machine and it just works. We open a secure connection for you. No extra tools, no port setup, no config.

Passwords never leak

Store a credential once and agents use it by role (like 'admin' or 'guest'). The raw password never comes back out of any tool, even when you rotate it.

Works with every MCP client

Claude Desktop, Claude Code, Cursor, LangChain, or your own client. One API key, no lock-in, no per-client setup.

The whole platform, as tools

Projects, environments, credentials, and run history are all first-class tools. Your agent can set up a new project, run a test, and inspect the result in one session.

Cancel runaway runs

If an agent goes sideways, call cancel_execution and the browser session stops cleanly. No burned tokens, no stuck processes.

Free to start

Grab an API key at app.debugg.ai, npx the server, and go. No credit card, no trial clock, no seat minimums.

Test locally, no setup

Test the version on your machine, right from your AI assistant. No setup.

Most browser MCPs only reach public URLs. To test the app on your machine, you have to wire up extra tunneling tools, copy-paste URLs, and hope the agent doesn't echo them back into logs.

DebuggAI's MCP AI server creates the tunnel for you on every check_app_in_browser call, and the tunnel URL never leaks back to the agent.

  • Point it at any URL on your machine, any port
  • A secure connection is created per run
  • Connection URLs never returned to the agent
  • Concurrent runs stay isolated

# You run your dev server

$ npm run dev

# Listening on http://localhost:3000

# Agent calls check_app_in_browser

url: "http://localhost:3000"

description: "Sign up with a new email, confirm redirect to /onboarding"

✓ Tunnel opened automatically

✓ Remote browser reached your dev server

✓ Agent signed up and was redirected

✓ Status: pass · Screenshot captured

The headline tool

check_app_in_browser

Give an AI agent eyes on a live website or app. The agent browses it, interacts with it, and tells you whether a given task or check passed.

Input parameters
Works on localhost or any URL. The only required params are description + url.
NameTypeRequiredPurpose
descriptionstring✅Natural language. What to test or evaluate.
urlstring✅Public URL or localhost (auto-tunneled)
environmentIdstringNoUUID of a saved environment
credentialIdstringNoUUID of a saved credential
credentialRolestringNoPick a credential by role (e.g. admin, guest)
usernamestringNoEphemeral login, not persisted server-side
passwordstringNoEphemeral login, not persisted, not logged
repoNamestringNoOverride auto-detected git repo name

8 tools, 21 actions, grouped by what they do

Not just a browser. The whole testing platform as tools your agent can call.

check_app_in_browser
headline

Run an AI browser agent against your app. It navigates, interacts, and reports pass or fail with screenshots.

description, url (+ optional environmentId, credentialId, credentialRole, username, password, repoName)

probe_page

Fast render check on 1 to 20 URLs: screenshot, console errors, and a network summary. No AI cost.

targets[] (+ per-URL waitForSelector, waitForLoadState, timeoutMs)

trigger_crawl

Crawl your app to build its knowledge graph, so future tests understand every page and flow.

url (+ optional environmentId, credentialId, projectUuid, timeoutSeconds)

Credentials

Logged-in flows without leaking passwords

You want an AI agent to test logged-in flows. You do not want the password showing up in a chat log or an error message.

DebuggAI stores credentials server-side. Agents reference them by UUID or role, so they never see the password itself.

  • Passwords go in, not out

    The raw password never appears in any tool response, even when an agent rotates it.

  • Pick credentials by role

    Pass credentialRole="admin" and the agent logs in without ever touching the credential directly.

  • Or skip the vault

    Pass username + password directly for a one-off run. Used once, never stored.

Create → use flow
// 1. Create the credential (password goes in)
create_credential({
  environmentId: "env-abc-123",
  label: "Admin test user",
  username: "admin@example.com",
  password: "supersecret",
  role: "admin"
})
// → { uuid: "cred-xyz", username, role }
//   password? nope, never echoed

// 2. Agent runs a test by role
check_app_in_browser({
  url: "http://localhost:3000",
  description: "Log in and open /admin",
  environmentId: "env-abc-123",
  credentialRole: "admin"
})
// → agent resolves the credential
//   server-side, never sees the password

// 3. Rotate. Still no echo
update_credential({
  uuid: "cred-xyz",
  environmentId: "env-abc-123",
  password: "new-supersecret"
})
// → { uuid, username, role }. No password
Execution history

Every run tracked. Every run cancellable.

Browser tests run in the background, so history and cancel are built in. Your agents can look back at what failed, and you can pull the plug on a run that's going sideways.

list_executions

Scroll back through every run your agents have made. Filter by status to find the failures.

get_execution

See exactly what the agent did step by step, so you (or another agent) can reason about why a test failed.

cancel_execution

Stop a runaway run before it burns more time or tokens. One call and the browser session closes cleanly.

Composed workflow

21 actions, one natural-language session

An AI agent setting up a new DebuggAI project from scratch and running its first test. Every step is a tool call.

// Agent: discover a team and a GitHub-linked repo
const { teams } = await list_teams({ q: "acme" })
const { repos } = await list_repos({ q: "checkout-service" })

// Create the project
const project = await create_project({
  name: "Checkout E2E",
  platform: "web",
  teamUuid: teams[0].uuid,
  repoUuid: repos[0].uuid,
})

// Add a staging environment + admin credential
const env = await create_environment({
  projectUuid: project.uuid,
  name: "staging",
  url: "https://staging.checkout.acme.dev",
})
await create_credential({
  environmentId: env.uuid,
  label: "Admin",
  username: "admin@acme.dev",
  password: process.env.ADMIN_PW,  // goes in, never comes back out
  role: "admin",
})

// Run a browser test by role, not by password
await check_app_in_browser({
  url: "http://localhost:3000",      // localhost auto-tunneled
  description: "Add item to cart, apply coupon CART20, checkout, verify receipt page",
  environmentId: env.uuid,
  credentialRole: "admin",
})

// Later: inspect or cancel from history
const { executions } = await list_executions({ status: "running", limit: 5 })
if (executions[0].durationMs > 120_000) {
  await cancel_execution({ uuid: executions[0].uuid })
}

How DebuggAI's MCP AI compares to other browser MCPs

Spot-check these claims against the linked repos before relying on them. They're moving targets.

FeatureDebuggAI MCP AIplaywright-mcppuppeteer-mcp
Tool count21~20~10
Tests the version on your machine
Saved credentials (passwords never returned)
Run history and mid-run cancel
Project and environment management
Managed remote browsers (no local Chrome)
Open sourceApache-2.0Apache-2.0Apache-2.0

Quickstart

Grab a free API key at app.debugg.ai, then wire up the MCP AI server in your client of choice.

npx
One command. Works with any MCP client.
DEBUGGAI_API_KEY=your_api_key npx -y @debugg-ai/debugg-ai-mcp
Config

One env var. That's it.

DEBUGGAI_API_KEY=your_api_key

Repo, branch, and file context are detected automatically from the folder you're in. No extra DEBUGGAI_LOCAL_* config required.

MCP AI server FAQ

Try the MCP AI server in five minutes

Free API key. Zero config beyond one env var. Works with every MCP client you already have.

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Made with 🩸, 💦, and 😭 in San Francisco

One AI QA team. Every way you build.

However you ship, DebuggAI tests your changes for you. Explore the ways to put it to work.

PR Copilot

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Real tests that write themselves from plain English.

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Real browser tests, without the Playwright setup.

AI PR Reviews

Catch bugs in every pull request before you merge.

User Acceptance Testing

Get real user sign-off on every release, faster.

Debugg.ai

AI that reviews your code to test your app in the browser & catch those frustrating UI issues that unit tests miss

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