MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
Beschreibung vom Entwickler (englisches Original)
QA-tests your site analytics, ad and consent tags (GA4, GTM, Ads, Meta, TikTok) from chat.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"ai-tag-debugger": {
"command": "npx",
"args": [
"-y",
"@analitikisler/ai-tag-debugger",
"mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add ai-tag-debugger -- npx -y @analitikisler/ai-tag-debugger mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"ai-tag-debugger": {
"command": "npx",
"args": [
"-y",
"@analitikisler/ai-tag-debugger",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"ai-tag-debugger": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@analitikisler/ai-tag-debugger",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto analitikisler bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.22.8) kam vorgestern in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
An MCP server retrieving transcripts of YouTube videos
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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:
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
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.
| Check | Example finding |
|---|---|
| Missing events | purchase never fired on the checkout journey |
| Missing parameters | purchase sent without value and currency on mobile |
| Invalid purchase value | purchase sent with value 0, a negative number or text such as "free" |
| Invalid purchase currency | purchase sent with currency usd or TL instead of a 3-letter ISO code such as USD or TRY |
| Empty purchase items | purchase sent with an empty items list |
| Purchase mismatch | GA4 sends value 120 and Meta sends 115 for the same order ID |
| Duplicates | add_to_cart sent twice by one click |
| Consent | Meta 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 data | An email address sent to an ad platform in plain text |
| Unplanned events | Events 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 coverage | Each planned event sorted into implemented, missing, missing parameters, wrong values or not checked (see Plan vs. site) |
| Campaign parameters | utm_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 cookies | A 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 site | A 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 tracking | One 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 tracking | After 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.
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.
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.
Nur der Anfang wird gezeigt; den Rest findest du im Repo