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PopNx MCPAI를 쓰는 사람을 위한 MCP 서버 · 쉬운 말로 설명해요

ai tag debugger

제작자: analitikisler버전 0.22.8i★ –i내 컴퓨터에서 실행
레지스트리 이름io.github.analitikisler/ai-tag-debugger

설치

개요

제작자가 쓴 설명(영어 원문)

QA-tests your site analytics, ad and consent tags (GA4, GTM, Ads, Meta, TikTok) from chat.

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: 내 컴퓨터에서 실행
  • 전송 방식: stdio
  • 카테고리: 마케팅 및 소셜 미디어

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
  • 레지스트리에 입력해야 할 키가 나와 있지 않아요.

필요한 것

  • npmNode.js 설치(npx 명령어용)

어떤 앱을 쓰세요?

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요

{
  "mcpServers": {
    "ai-tag-debugger": {
      "command": "npx",
      "args": [
        "-y",
        "@analitikisler/ai-tag-debugger",
        "mcp"
      ]
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add ai-tag-debugger -- npx -y @analitikisler/ai-tag-debugger mcp

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "mcpServers": {
    "ai-tag-debugger": {
      "command": "npx",
      "args": [
        "-y",
        "@analitikisler/ai-tag-debugger",
        "mcp"
      ]
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "servers": {
    "ai-tag-debugger": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@analitikisler/ai-tag-debugger",
        "mcp"
      ]
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 GitHub 계정 analitikisler 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • – 해당 없음

    엔드포인트 응답

    내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요

  • – 해당 없음

    OAuth로 로그인

    내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

마케팅 및 소셜 미디어 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 3566개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 3566개 중 91%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 3566개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

ai tag debugger, 안전한가요?

해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.22.8)은 그저께 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

MCP YouTube Transcript

제작자: jkawamoto

An MCP server retrieving transcripts of YouTube videos

내 컴퓨터에서 실행★ 495MIT10일 전 업데이트
설치

Google Analytics MCP

제작자: surendranb

GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.

내 컴퓨터에서 실행★ 244MIT10일 전 업데이트
설치

AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.

클라우드에서 실행★ 137MIT11일 전 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

English | Türkçe

An open-source AI agent that QA-tests your website's analytics and ad tracking.

An AI model drives a real browser through your key journeys (product → cart → checkout, lead forms) like a real visitor. While it browses, ai-tag-debugger records every tracking hit (GA4, Google Ads, Meta Pixel, TikTok and about a dozen more platforms) and every dataLayer push. It then checks them against your measurement plan and Consent Mode setup and writes a plain-language report of what broke and how to fix it, in English or Turkish.

Use it in two ways:

  • From your terminal or CI, with your own API key for Anthropic (Claude), OpenAI, Google Gemini, or any OpenAI-compatible API (Kimi, GLM, DeepSeek, OpenRouter, a local Ollama and more).
  • From your AI chat app (Claude, ChatGPT, Gemini and other apps that support MCP). The app's own model drives the audit, so you don't need a separate API key.

Built by Analitik İşler. MIT licensed.

▶ checkout (mobile)

Accept cookies Open the first product Add to cart Open the cart Go to checkout … Place the order ✓ All scripted steps completed. · 20 tracking hits captured

2 broken · 1 risk · 1 warning · 4 ok Report: reports/demo/localhost/runs/2026-10-16_0800/report.html

Why

Tracking breaks silently. A redesign renames a button, a new template forgets a field, a consent banner update lets pixels fire too early. Nobody gets an alert. You find out weeks later, after ad platforms have optimized on bad conversions.

Manual tag QA catches this, but it's tedious, so it rarely happens after every release. ai-tag-debugger makes it a command you can run in CI.

What it checks

CheckExample finding
Missing eventspurchase never fired on the checkout journey
Missing parameterspurchase sent without value and currency on mobile
Invalid purchase valuepurchase sent with value 0, a negative number or text such as "free"
Invalid purchase currencypurchase sent with currency usd or TL instead of a 3-letter ISO code such as USD or TRY
Empty purchase itemspurchase sent with an empty items list
Purchase mismatchGA4 sends value 120 and Meta sends 115 for the same order ID
Duplicatesadd_to_cart sent twice by one click
ConsentMeta PageView fired before the visitor accepted cookies, Consent Mode default missing or set too late, Consent Mode v2 signals (ad_user_data, ad_personalization) missing, tracking cookies set before consent, tags or cookies still active after the decline_cookies journey rejects the banner, a reject button that does nothing, a Consent Mode default the page pushed that the Google tag never applied (for example pushed as a plain array instead of through gtag()). Defaults and updates a consent platform sets inside GTM, which never show in the dataLayer, are read from the Google tag's own state
Personal dataAn email address sent to an ad platform in plain text
Unplanned eventsEvents firing that aren't in your measurement plan. GA4's automatic events (first_visit, session_start, user_engagement) and enhanced measurement events (page_view, scroll, click, view_search_results, video_*, file_download, form_start, form_submit) never count, since no tag sends them. When one of them is planned and missing, the finding names the enhanced measurement setting that sends it; first_visit and session_start are read from the flags GA4 puts on its other hits.
Plan coverageEach planned event sorted into implemented, missing, missing parameters, wrong values or not checked (see Plan vs. site)
Campaign parametersutm_campaign or gclid on the landing address dropped by a redirect, or missing from the page address the hits report. The visit would be credited to direct traffic
Click id and client id cookiesA Google Ads tag ran with ad storage granted, but _gcl_aw never held the landing page's gclid, so conversions can't be tied to the click; or the GA4 client id in _ga changed mid-visit, which splits one visitor into two users
Pages that aren't the siteA bot challenge (Cloudflare, DataDome, HUMAN, Akamai) shown instead of the page, or a page that crashed in the browser, such as a Next.js "Application error" after its own scripts came back as HTML. Events not seen on such a run are unverified rather than missing
Server-side trackingOne event ID used for two different events, or a Meta event ID that doesn't match the event_id on the GA4 hit a server container turns into a Conversions API event
Logged-in trackingAfter logging in with the test account, GA4 hits went out without the site's user ID (uid), so GA4 can't tie the visit to the account

Platforms: GA4 (including server-side GTM endpoints and Google tag gateway paths on the site's own domain; hits to a server container are marked server-side, and Stape and Elevar requests are recognised), Google Ads conversions, Meta Pixel, TikTok Pixel, basic X Pixel events, LinkedIn Insight Tag, Microsoft Advertising UET, Criteo OneTag, and Pinterest, Snapchat and Klaviyo events. Adobe Analytics, Matomo and Piwik PRO events are read too (page views, links, goals, site search and ecommerce orders), and Microsoft Clarity and Hotjar tag loads name the project or site id.

When GA4 hits go to a server container on another host, or its loader is served from one, the report adds an info finding: the events it forwards to ad platforms never pass through the browser, so they can't be seen here.

With a gtm_container export, each broken event also names the GTM tag that sends it and the trigger it fires on, or says that no tag sends it. Each tag then says whether its trigger fired in that journey and, when it did not, why: the tag is paused, no dataLayer push carried the trigger's event, a trigger condition failed (with the value the push had, read from the cumulative dataLayer the way GTM's Data Layer Variables read it), an exception trigger blocked it, or the consent it needs was denied when the event fired. When everything fired and the hit is still wrong, it says so, pointing at the tag's own setup. Click, form, visibility and timer triggers are evaluated by GTM in the page, so they are reported as not evaluable unless their gtm.click-style event reached the dataLayer. All of this is evaluated from the captured dataLayer, with no AI model.

Choose the platforms to check

By default an audit checks GA4, Google Ads, Meta and TikTok, and also any other platform the site turns out to use. That platform is checked from its first request, with no setting needed. Choosing platforms turns this detection off. Pick a fixed list with --platforms ga4,meta,x, "providers" in the config, or providers on the MCP start_audit and run_audit tools. You can choose from ga4, google_ads, meta, tiktok, x, linkedin, microsoft_ads, pinterest, snapchat, criteo, klaviyo, adobe, matomo, piwik_pro, clarity, hotjar, doubleclick and adsense. Events are parsed for GA4, Google Ads, Meta, TikTok, X, LinkedIn, Microsoft Ads, Pinterest, Snapchat, Criteo, Klaviyo, Adobe Analytics, Matomo and Piwik PRO. Clarity and Hotjar show their tag loads, and the others show their network requests. --providers still works as the old name of --platforms for one more minor version and prints a note.

Requests to platforms you didn't choose are still answered locally, so they never reach those accounts either. They are left out of the timeline, the CSV and the checks, and only counted: the report lists them with their request counts (for example "61 out-of-scope requests were skipped: Microsoft Clarity, DoubleClick"), and each step says how many it skipped. Without --platforms, any platform your measurement plan names or the site sends to is added to the default list. Planned events for platforms you did leave out are listed in one info finding instead of being reported as missing. Tag manager and consent platform requests are always shown.

Consent scenarios

Each audit also opens the start page once per consent choice, each time in a fresh browser and like an ad click (with the same test campaign parameters as the journeys, unless campaign_checks is false): no choice and on to the next page, accept all, reject all and analytics only. The choice goes through the consent platform's own API (OneTrust, Cookiebot, Usercentrics, Didomi or Efilli) or, for CookieYes, its named buttons and switches, or else by clicking the banner's buttons. The report then shows how many hits each tag sent after each choice. Analytics only works with OneTrust (it turns on the group OneTrust's Consent Mode setting gives analytics_storage), Cookiebot and CookieYes, and with Efilli when its own Consent Mode integration is on (it turns on the category Efilli maps analytics_storage to); with other platforms that column says "Not supported here". A tag that sends after reject all, or on the next page when the visitor made no choice (browsing on is no consent under the GDPR or KVKK), is flagged, except cookieless Consent Mode pings. An advertising tag that sends after analytics only is flagged unless it was sent with ad storage denied. The table is at the top of the Journeys tab and above the findings in report.md. It is skipped when the page shows no banner the tool can answer, and when a saved site is replayed.

Under it, the Consent matrix puts the choices side by side, from the page each one leads to: the first Google hit and the consent it carries, whether the campaign parameters reached it, whether the GA4 client id the visitor had before choosing was kept, whether _gcl_aw holds the landing gclid, the cookies set, how long after the page started each tag was called, and the Google tag's own consent. What should not happen with a choice is marked: tracking cookies before a choice, without one or after reject all, a client id that changes on accept, and a UTM parameter lost from the first hit. The same matrix is in report.md, and the consent_matrix MCP tool runs it on its own.

The Consent section also names the consent platform the site runs. For Efilli it says when the site's bundle was compiled, which language's setup is in force, its categories, whether Efilli's own Consent Mode integration is on and the category each signal maps to (and, when it is off, that the signals come from somewhere else, such as a GTM template), and whether auto-blocking and IAB TCF are on. For CookieYes it gives the law its banner follows, its categories, how it maps them to Consent Mode (the same on every site: advertisement to ad_storage, ad_user_data and ad_personalization, analytics to analytics_storage, functional to functionality_storage and personalization_storage, necessary to security_storage), how many providers it holds back, and when its site script last changed. For OneTrust: its version, the consent model and region it applied to the visit, its groups, whether its Consent Mode setting is on and which group grants each signal, ads data redaction, IAB TCF, and when the site last published its setup. For Cookiebot: its version, consent model and region, its fixed mapping (marketing to the three ad signals, statistics to analytics_storage, preferences to functionality_storage and personalization_storage), whether its Consent Mode integration is on, ads data redaction, auto-blocking, IAB TCF, and when its setup was last published.

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