Track MCP LogoTrack MCP
Track MCP LogoTrack MCP

The world's largest repository of Model Context Protocol servers. Discover, explore, and submit MCP tools.

Product

  • Categories
  • Top MCP
  • New & Updated
  • Submit MCP

Company

  • About

Legal

  • Privacy Policy
  • Terms of Service
  • Cookie Policy

© 2026 TrackMCP. All rights reserved.

Built with ❤️ by Krishna Goyal

    Debugg Ai Mcp

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

    65 stars
    TypeScript
    Updated Oct 18, 2025
    automation
    browser
    end-to-end-testing
    web

    Table of Contents

    • Setup
    • Tools
    • Browser
    • check_app_in_browser
    • trigger_crawl
    • probe_page
    • project
    • environment
    • test_suite
    • test_case
    • executions
    • Pagination
    • Resources
    • Security invariants
    • Migration to v3.0.0 (action-based tools)
    • Migration from v1.x (breaking change in v2.0.0)
    • Configuration
    • Remote / HTTP transport (optional)
    • Telemetry
    • Local Development
    • MCP registration: debugg-ai-local vs debugg-ai
    • Links

    Table of Contents

    • Setup
    • Tools
    • Browser
    • check_app_in_browser
    • trigger_crawl
    • probe_page
    • project
    • environment
    • test_suite
    • test_case
    • executions
    • Pagination
    • Resources
    • Security invariants
    • Migration to v3.0.0 (action-based tools)
    • Migration from v1.x (breaking change in v2.0.0)
    • Configuration
    • Remote / HTTP transport (optional)
    • Telemetry
    • Local Development
    • MCP registration: debugg-ai-local vs debugg-ai
    • Links

    Documentation

    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:

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

    Or with Docker:

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

    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.

    ParameterTypeDescription
    descriptionstring requiredWhat to test (natural language)
    urlstring requiredTarget URL — http://localhost:3000 is auto-tunneled
    environmentIdstringUUID of a specific environment
    credentialIdstringUUID of a specific credential
    credentialRolestringPick a credential by role (e.g. admin, guest)
    usernamestringUsername for login (ephemeral — not persisted)
    passwordstringPassword for login (ephemeral — not persisted)
    repoNamestringOverride 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.

    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:

    json
    "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.

    ParameterTypeDescription
    targetsarray required1-20 entries: [{url, waitForSelector?, waitForLoadState?, timeoutMs?}]
    targets[].urlstring requiredPublic URL or localhost (auto-tunneled)
    targets[].waitForLoadStateenum'load' (default) / 'domcontentloaded' / 'networkidle'
    targets[].waitForSelectorstringOptional CSS selector to wait for after navigation
    targets[].timeoutMsnumberPer-URL timeout, 1000-30000 (default 10000)
    includeHtmlbooleanReturn raw HTML in each result (default false)
    captureScreenshotsbooleanReturn one PNG per target (default true)

    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: ": [ ... ]

    }

    code
    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

    code
    ## 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

    code
    It is an OAuth **Resource Server**: every `POST /mcp` needs
    `Authorization: Bearer `; 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.
    
    ## 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)

    code
    ## Local Development

    npm install

    npm run build

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

    code
    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//`. 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

    code
    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
    
    [Dashboard](https://app.debugg.ai) · [Docs](https://debugg.ai/docs) · [Issues](https://github.com/debugg-ai/debugg-ai-mcp/issues) · [Discord](https://debugg.ai/discord)
    
    ---
    
    Apache-2.0 License © 2025 DebuggAI

    Similar MCP

    Based on tags & features

    • MC

      Mcp Server Browserbase

      TypeScript·
      2.7k
    • OP

      Openai Gpt Image Mcp

      TypeScript·
      75
    • MC

      Mcgravity

      TypeScript·
      71
    • PL

      Pluggedin Mcp Proxy

      TypeScript·
      97

    Trending MCP

    Most active this week

    • PL

      Playwright Mcp

      TypeScript·
      22.1k
    • SE

      Serena

      Python·
      14.5k
    • MC

      Mcp Playwright

      TypeScript·
      4.9k
    • MC

      Mcp Server Cloudflare

      TypeScript·
      3.0k
    View All MCP Servers

    Similar MCP

    Based on tags & features

    • MC

      Mcp Server Browserbase

      TypeScript·
      2.7k
    • OP

      Openai Gpt Image Mcp

      TypeScript·
      75
    • MC

      Mcgravity

      TypeScript·
      71
    • PL

      Pluggedin Mcp Proxy

      TypeScript·
      97

    Trending MCP

    Most active this week

    • PL

      Playwright Mcp

      TypeScript·
      22.1k
    • SE

      Serena

      Python·
      14.5k
    • MC

      Mcp Playwright

      TypeScript·
      4.9k
    • MC

      Mcp Server Cloudflare

      TypeScript·
      3.0k