Debugg AI

Debugg AI — MCP Server

AI-powered browser testing via the Model Context Protocol. Point it at any URL (or localhost) and describe what to test — an AI agent browses your app and returns pass/fail with screenshots.

Setup

Requires Node.js 20.20.0 or later (transitive requirement from posthog-node@^5.26.0).

Testing http://localhost:... URLs requires the caddy binary — check_app_in_browser, probe_page, and trigger_crawl tunnel localhost targets through a local Caddy reverse proxy. This installs automatically: the @radically-straightforward/caddy npm dependency downloads a pinned Caddy release for your platform during npm install/npx, same as this project already does for the ngrok binary — nothing to install yourself in the normal case. If that download never ran (npm install --ignore-scripts, an offline/air-gapped install), point CADDY_BIN at your own install (brew install caddy / apt install caddy / see caddyserver.com/docs/install) — missing it surfaces as a clear error on the first localhost-URL call, not a silent hang. Public-URL calls, every non-browser tool, and test_suite {action:"run"} (which uses its own dedicated tunnel and bypasses Caddy entirely) don't need it either way.

Get an API key at debugg.ai, then add to your MCP client config:

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

Or with Docker:

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

The Dockerfile's npm install step would pick up caddy the same automatic way local installs do, in principle — but as of this writing the Dockerfile doesn't COPY several directories the build now needs (handlers, tools, types, config) and still references a tunnels/ directory that no longer exists, so a fresh build likely fails before that matters. That's a pre-existing gap, unrelated to Caddy. The currently published quinnosha/debugg-ai-mcp image predates the Caddy dependency regardless — localhost-URL calls to check_app_in_browser/probe_page/trigger_crawl will fail with CaddyBinaryNotFoundError inside that image until it's rebuilt (Dockerfile fixed) and republished, or CADDY_BIN points at one baked in separately. Public-URL calls, the non-browser tools, and test_suite {action:"run"} are unaffected either way.

Tools

The server exposes 8 tools: three Browser tools plus one action-based tool per managed entity. The headline tools are check_app_in_browser (full AI agent) and probe_page (lightweight no-LLM page probe). The rest — project, environment, test_suite, test_case, executions — each take an action discriminator (e.g. {"action":"list"}) that selects the operation. Destructive delete actions require confirmation (an elicitation prompt where supported, otherwise confirm: true).

Browser

check_app_in_browser

Runs an AI browser agent against your app. The agent navigates, interacts, and reports back with screenshots. Localhost URLs are auto-tunneled via ngrok.

Parameter	Type	Description
description	string required	What to test (natural language)
url	string required	Target URL — http://localhost:3000 is auto-tunneled
environmentId	string	UUID of a specific environment
credentialId	string	UUID of a specific credential
credentialRole	string	Pick a credential by role (e.g. admin, guest)
username	string	Username for login (ephemeral — not persisted)
password	string	Password for login (ephemeral — not persisted)
loginCredentials	array	Accounts for logins the agent hits during the task — [{username, password, label?}]
useEnvironmentCredentials	boolean	Default true. false forbids auto-filling the environment's stored credentials
auth	object	Auth precondition — {precondition, entryUrl, deepUrl, environmentId, username, password}
repoName	string	Override auto-detected git repo name (e.g. my-org/my-repo)

One focused check per call. The agent has a ~25-step internal budget; split broader suites across multiple calls.

Credentials: pass them as parameters, not prose

Naming an account only in description does not make the agent use it — it falls back to the environment's stored credential, and the app's rejection of the wrong account comes back looking like an application failure. Anything you pass as a parameter beats the environment default for every login in the run, not just the first:

• username / password (or credentialId / credentialRole) — the run's identity.
• auth.username / auth.password — pins the precondition login when you also use auth.precondition: "login".
• loginCredentials — accounts for a login form the agent reaches part-way through the task. This is the one for flows like set a password → get bounced to sign-in → log in as the account you just created, where splitting into separate calls would lose browser state.

Set useEnvironmentCredentials: false when a silent fallback to the default test user would invalidate the check. The call is rejected if you opt out without naming an account, since the run would have no way to authenticate.

Results report the identity actually used, so a wrong one is visible rather than masquerading as a broken app:

"logins": [
  { "username": "qa+invitefix@example.com", "source": "task", "submitted": true, "authenticated": true }
],
"credentialWarning": {
  "requested": "qa+invitefix@example.com",
  "used": ["qatest123@example.com"],
  "message": "This run signed in with an environment default credential even though '…' was specified. …"
}

source is task | explicit | credential_id (an account you named) or env | env_default (the environment's stored account). credentialWarning appears only when you named an account and an environment default was used anyway. loginError appears when a named account could not be resolved and the run declined to substitute a different one.

Every successful run returns a browserSession block alongside the screenshot — presigned S3 URLs for the captured HAR (full network trace) and console log (every JS console message). Use them to detect refetch loops, hydration errors, and other runtime issues that pass type-checks and unit tests:

"browserSession": {
  "harUrl": "https://...session_18139.har?X-Amz-...",
  "consoleLogUrl": "https://...session_18139_console.json?X-Amz-...",
  "recordingUrl": "https://...session_18139_recording.webm?X-Amz-...",
  "harStatus": "downloaded",
  "consoleLogStatus": "downloaded",
  "harRedactionStatus": "redacted",
  "consoleLogRedactionStatus": "redacted"
}

URLs are short-lived presigned S3 — refetch the parent execution via executions {action:"get", uuid} to renew. harStatus / consoleLogStatus disambiguate 'downloaded' (URL fetchable), 'not_available' (page emitted nothing), 'failed' (capture broke). On a fresh run the URLs are commonly null because capture uploads async after the agent finishes — poll executions {action:"get", uuid: executionId} until status reaches 'downloaded'. Authorization / Cookie / token/secret/api_key headers are scrubbed server-side before the artifacts are persisted.

trigger_crawl

Fires a server-side browser-agent crawl to populate the project's knowledge graph. Localhost URLs tunnel automatically. Returns {executionId, status, targetUrl, durationMs, outcome?, crawlSummary?, knowledgeGraph?, browserSession?} with knowledgeGraph.imported === true on successful ingestion. The browserSession block (HAR + console-log URLs, same shape as above) is also present on completed crawls.

probe_page

Lightweight no-LLM batch page probe. Pass 1-20 URLs; each navigates, waits for load, and returns rendered state — screenshot + page metadata + structured console errors + network summary. No agent loop, no LLM cost, no scenario assertions. Use it for "did I just break /settings?", multi-route smoke after a refactor, CI per-PR sweeps, and quick is-it-up checks where check_app_in_browser's 60-150s agent loop is overkill.

Parameter	Type	Description
targets	array required	1-20 entries: [{url, waitForSelector?, waitForLoadState?, timeoutMs?}]
targets[].url	string required	Public URL or localhost (auto-tunneled)
targets[].waitForLoadState	enum	'load' (default) / 'domcontentloaded' / 'networkidle'
targets[].waitForSelector	string	Optional CSS selector to wait for after navigation
targets[].timeoutMs	number	Per-URL timeout, 1000-30000 (default 10000)
includeHtml	boolean	Return raw HTML in each result (default false)
captureScreenshots	boolean	Return one PNG per target (default true)

All targets in a batch share one session tunnel, but only same-port (or all-public) batches share a single backend execution — 5 URLs on one port in one call is dramatically faster than 5 parallel single-URL calls. A batch that mixes multiple local ports decomposes into one sequential backend execution per port group (still one call, still one merged results[] in your original order, but N backend round-trips instead of one — slower, not rejected). Per-URL error field preserves batch resilience: a single failed target doesn't fail the others.

networkSummary aggregation key is origin + pathname — refetch loops (?n=0..4 repeatedly hitting the same endpoint) collapse into a single entry with the count, so /api/poll showing up with count: 47 is the actionable "infinite refetch loop" signal users originally asked for.

Performance budget: <10s for 1 URL, <25s for 20. Localhost dead-port returns LocalServerUnreachable in <2s without burning a workflow execution.

project

Action	Params	Result
get	{uuid}	Curated project detail
list	{q?, page?, pageSize?}	Paginated summaries
create	{name, platform, (teamUuid|teamName), (repoUuid|repoName)}	Created project

Team and repo resolve by either uuid or name (case-insensitive exact match; NotFound if none, AmbiguousMatch if multiple). There is no update/delete — rename or delete a project from the DebuggAI web app.

environment

Action	Params	Result
get	{uuid, projectUuid?}	Env with credentials inlined (passwords never returned)
list	{projectUuid?, q?, page?, pageSize?}	Paginated envs, each with a credentials array
create	{name, url, description?, projectUuid?, credentials?}	Created env (optionally seeds credentials)
update	{uuid, name?, url?, description?, addCredentials?, updateCredentials?, removeCredentialIds?}	Patched env; credential ops run remove → update → add
delete	{uuid, projectUuid?, confirm?}	Deletes env (cascades credentials) — requires confirmation

projectUuid auto-resolves from the git repo when omitted. Per-cred failures surface in credentialWarnings[] without blocking the env op.

test_suite

Action	Params	Result
list	{projectUuid|projectName, search?, page?, pageSize?}	Paginated suites with status + pass rate
create	{name, description, projectUuid|projectName}	Created suite
run	{suiteUuid|(suiteName+project), targetUrl?}	Triggers all tests async
results	{suiteUuid|(suiteName+project)}	Suite + per-test outcomes
delete	{suiteUuid|(suiteName+project), confirm?}	Soft-delete — requires confirmation

test_case

Action	Params	Result
create	{name, description, agentTaskDescription, suiteUuid|(suiteName+project), relativeUrl?, maxSteps?}	Created test case (not auto-run)
update	{testUuid, name?, description?, agentTaskDescription?}	Patched test case
delete	{testUuid, confirm?}	Soft-delete — requires confirmation

executions

Action	Params	Result
get	{uuid}	Full detail (nodeExecutions + state + errorInfo) + screenshot/gif artifacts
list	{status?, projectUuid?, page?, pageSize?}	Paginated summaries

404 from the backend surfaces as isError: true with {error: 'NotFound', message, uuid}. Credentials are always returned without passwords.

Pagination

Every filter-mode response is paginated. Response shape:

{
  "filter": { "...echoed query params..." },
  "pageInfo": { "page": 1, "pageSize": 20, "totalCount": 47, "totalPages": 3, "hasMore": true },
  "<items>": [ ... ]
}

Pass optional page (1-indexed, default 1) and pageSize (default 20, max 200; oversized values are clamped). No response is ever silently truncated.

Resources

Alongside tools, the server exposes the read-only entities as MCP resources so clients can browse and @-mention them as context:

URI	What
debugg-ai://projects	All projects (first page)
debugg-ai://environments	Environments for the auto-detected project
debugg-ai://executions	Recent executions (first page)
debugg-ai://project/{uuid}	One project, full detail
debugg-ai://environment/{uuid}	One environment (credentials inline, passwords redacted)
debugg-ai://execution/{uuid}	One execution, full node detail + artifact links

Reads dispatch to the same handlers as the project / environment / executions tools, so the data and auth are identical. Resources are additive — clients without resource support keep using the tools.

Security invariants

• Passwords are write-only. They never appear in any response body from any tool.
• Tunnel URLs (*.ngrok.debugg.ai) are stripped from all browser-agent responses, including agent-authored text.
• 404s from the backend surface as isError: true with {error: 'NotFound', ...}, never as thrown exceptions.
• Missing DEBUGGAI_API_KEY surfaces as a structured tool error on first invocation — the server still registers and lists tools normally.

Migration to v3.0.0 (action-based tools)

v3 consolidated the 20 per-verb tools into 8 action-based tools. Old tool → new tool {action}:

Removed	Replacement
search_projects	project {action:"get"} / project {action:"list"}
create_project	project {action:"create"}
update_project, delete_project	Dropped — use the DebuggAI web app
search_environments	environment {action:"get"} / {action:"list"}
create_environment / update_environment / delete_environment	environment {action:"create"|"update"|"delete"}
create_test_suite / search_test_suites / run_test_suite / get_test_suite_results / delete_test_suite	test_suite {action:"create"|"list"|"run"|"results"|"delete"}
create_test_case / update_test_case / delete_test_case	test_case {action:"create"|"update"|"delete"}
search_executions	executions {action:"get"|"list"}
trigger_crawl headless param	Dropped — always headless

delete actions now require confirmation (elicitation prompt, or confirm: true). Clients pick up the new surface on MCP restart.

Migration from v1.x (breaking change in v2.0.0)

v2 collapsed a 22-tool surface to 11. Old-tool → new-tool mapping:

Removed	Replacement
list_projects, get_project	search_projects (uuid mode vs filter mode)
list_environments, get_environment	search_environments
list_credentials, get_credential	search_environments — credentials inline on each env
create_credential	create_environment({credentials: [...]}) seed, or update_environment({addCredentials: [...]})
update_credential	update_environment({updateCredentials: [{uuid, ...patch}]})
delete_credential	update_environment({removeCredentialIds: [uuid]})
list_teams, list_repos	create_project({teamName, repoName}) — name resolution with ambiguity handling
list_executions, get_execution	search_executions
cancel_execution	Dropped — backend spin-down is automatic

Response-shape changes: the bare count field on list responses is gone — use pageInfo.totalCount.

Configuration

Env var	Required	Purpose
DEBUGGAI_API_KEY	yes	Backend API key. Aliases: DEBUGGAI_API_TOKEN, DEBUGGAI_JWT_TOKEN.
DEBUGGAI_API_URL	no	Backend base URL. Defaults to https://api.debugg.ai.
DEBUGGAI_TOKEN_TYPE	no	token (default) or bearer.
DEBUGGAI_EVAL_TEMPLATE	no	Override the App Evaluation workflow slug that check_app_in_browser dispatches to. Defaults to flow/e2es/app-eval. Dispatch pins to this slug so a backend template rename can't break it.
LOG_LEVEL	no	error / warn / info (default) / debug.
POSTHOG_API_KEY	no	Override the embedded telemetry project key (e.g. private fork).
DEBUGGAI_TELEMETRY_DISABLED	no	Set to 1 / true / yes / on to disable telemetry entirely.

DEBUGGAI_API_KEY=your_api_key

Remote / HTTP transport (optional)

By default the server speaks stdio (local npx). It can instead run as a hosted, multi-user remote MCP over stateless Streamable HTTP + OAuth:

DEBUGGAI_MCP_TRANSPORT=http PORT=3000 DEBUGGAI_TOKEN_TYPE=bearer npx -y @debugg-ai/debugg-ai-mcp@latest

It is an OAuth Resource Server: every POST /mcp needs Authorization: Bearer <token>; missing/invalid tokens get a 401 with a WWW-Authenticate pointing at the RFC 9728 metadata, and clients run the OAuth flow against the advertised authorization server. The bearer is request-scoped — api.debugg.ai validates it.

Endpoint	Purpose
POST /mcp	MCP Streamable HTTP (bearer-protected)
GET /.well-known/oauth-protected-resource	RFC 9728 metadata (authorization server discovery)
GET /health	Load-balancer / ECS health check

Env var	Default	Purpose
DEBUGGAI_MCP_TRANSPORT	stdio	Set to http for the remote transport
PORT	3000	HTTP listen port
DEBUGGAI_MCP_PUBLIC_URL	https://mcp.debugg.ai	This server's public resource URL (RFC 9728 resource)
DEBUGGAI_OAUTH_ISSUER	https://auth.debugg.ai	Authorization server advertised to clients
DEBUGGAI_TOKEN_TYPE	token	Set to bearer so OAuth tokens forward as Authorization: Bearer

stdio installs need none of these.

Multi-replica deployments (go/no-go before rollout): tunnel state (the ngrok session tunnel, its Caddy instance, and its port-route lock) is in-process, keyed per caller by a hash of the bearer token — there is no cross-process coordination. Running several replicas behind a plain round-robin load balancer means one caller's calls can land on different replicas and mint one tunnel per replica they hit instead of one for the whole session (extra ngrok cost, bounded by replica count, self-healing via the existing 55-minute idle auto-shutoff — never a cross-session correctness bug, since any single tool call stays on one replica for its whole duration). To get the intended "one tunnel per session" behavior on a multi-replica HTTP deployment, configure session-affine routing at the load balancer (sticky/consistent-hash keyed on the same identity getSessionKey() derives — in practice, the caller's Authorization bearer token). See docs/local-tunnel-multiplexer-architecture-2026-07-31.md §2.1 for the full reasoning and the honest degrade path if this isn't configured.

Telemetry

The MCP server ships with telemetry enabled by default — an embedded write-only PostHog project key (phc_*) so the team can observe cache hit rates, poll cadence, tunnel reliability, and other operational metrics across the install base. Captured events:

Event	When
tool.executed / tool.failed	Per tool call
workflow.executed	Per browser-agent execution (carries pollCount, durationMs, finalIntervalMs)
tunnel.provisioned / tunnel.provision_retry / tunnel.stopped	Per tunnel lifecycle event
template.lookup / project.lookup	Cache hit/miss with durationMs on cold-call

Privacy posture:

• The distinct ID is SHA-256(api_key).slice(0, 16) — never the raw key, no PII.
• phc_* keys are write-only by PostHog convention; safe to embed in source.
• Set DEBUGGAI_TELEMETRY_DISABLED=1 to opt out entirely (resolves to a no-op provider; no events leave the process).

The active mode is logged at boot:

Telemetry enabled (PostHog, DebuggAI default project). Set DEBUGGAI_TELEMETRY_DISABLED=1 to opt out.
Telemetry enabled (PostHog, custom POSTHOG_API_KEY)
Telemetry disabled (DEBUGGAI_TELEMETRY_DISABLED is set)

Local Development

npm install
npm run build
npm run test:e2e        # real end-to-end evals against the backend

The eval suite spawns the built MCP server as a subprocess, exercises every tool against a real backend, and writes per-flow artifacts to scripts/evals/artifacts/<timestamp>/. See scripts/evals/flows/ for the individual scenarios.

MCP registration: debugg-ai-local vs debugg-ai

This repo ships a .mcp.json that registers a project-scoped server named debugg-ai-local pointing at node dist/index.js — the freshly-built local code. It only activates when Claude Code's working directory is this repo.

Your other projects should use the user-scoped debugg-ai registration that pulls from the published npm package:

npm run mcp:global      # registers debugg-ai in ~/.claude.json to npx -y @debugg-ai/debugg-ai-mcp

After editing code here, run npm run mcp:local (which just rebuilds) so the next invocation of debugg-ai-local picks up your changes.

Links

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