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).

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

Related MCP server: mcp-optimizer

Tools

The server exposes 12 tools grouped into Browser (3), Search (3), Projects (3), and Environments (3). The headline tools are check_app_in_browser (full AI agent) and probe_page (lightweight no-LLM page probe); the rest manage projects, environments + their credentials, and execution history through a uniform search_* + CRUD pattern.

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.

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

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 search_executions 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 search_executions with the returned 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.

The whole batch shares a single backend execution + browser session + tunnel — 5 URLs in one call is dramatically faster than 5 parallel single-URL calls. 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.

Search (dual-mode: uuid detail OR filtered list)

Each search_* tool has two modes. Pass {uuid} for a single-record detail response. Pass filter params for a paginated summary list. 404 from the backend surfaces as isError: true with {error: 'NotFound', message, uuid}.

projectUuid is optional on search_environments — if omitted, it auto-resolves from the git repo. Credentials are always returned without passwords.

Projects

Environments (credential sub-actions folded in)

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.

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

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

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

Configuration

DEBUGGAI_API_KEY=your_api_key

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:

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.

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