Use Cases: How Teams Use AdTechToolkit

The problem

A video advertising campaign is about to go live, but the QA team needs to verify that the VAST tags will load correctly, play the right creative, and fire all tracking events. Manually testing each tag by loading it in a player and watching network requests is slow and unreliable.

The solution

The team uses the VAST Inspector to paste each tag URL, automatically follow wrapper chains, parse the resulting XML, and simulate playback with the Google IMA SDK. The analysis panel shows media files, tracking events, and wrapper depth in a single view, while the event log confirms that impressions, quartiles, and completion events fire correctly.

The outcome

Tags are validated in minutes instead of hours. Issues like broken wrapper URLs, missing media files, and misconfigured tracking pixels are caught before the campaign serves a single impression, preventing revenue loss and discrepancy disputes.

The problem

A campaign manager reports that click-through URLs are leading to error pages instead of the advertiser's landing page. The tracking URL passes through multiple domains — the ad server, a click tracker, a URL shortener, and the destination — and the team does not know which hop is failing.

The solution

The URL Redirect Resolver traces the full chain hop by hop, showing the HTTP status code, response time, and destination URL at each step. The team immediately sees that the third hop returns a 502 Bad Gateway, identifying the failing domain without manually following each redirect in a browser.

The outcome

The broken hop is escalated to the responsible vendor with specific evidence (status code, URL, timing). The fix is deployed within the hour, and the team re-traces the chain to confirm the landing page is now reachable before the campaign resumes.

The problem

An engineering team is updating a backend service that produces JSON API responses consumed by multiple downstream clients. Before deploying the update, they need to verify that the response structure has not changed unexpectedly — no missing fields, no type changes, no unintended additions.

The solution

The team captures the API response from the current production endpoint and the staging endpoint, then pastes both into the JSON Diff tool. With normalization enabled (to ignore key ordering), the inline diff highlights exactly which fields were added, removed, or modified. The diff summary provides a quick count of changes for the pull request description.

The outcome

The team confirms that only the intended fields changed, with no regressions in the response structure. The diff output is attached to the deployment ticket as validation evidence, giving stakeholders confidence in the release.

The problem

A support engineer receives a bug report with a Base64-encoded error token from an SDK log. The token contains debugging information, but the engineer needs to decode it to understand what went wrong. The token also appears in a URL parameter that has been double-encoded, making it unreadable.

The solution

The engineer uses the Base64 / URL Encoder tool to first URL-decode the parameter (removing the percent-encoding), then Base64-decode the resulting string. The tool's mode selector makes it easy to chain these operations: URL decode first, then switch to Base64 decode. The decoded payload reveals the error message and stack trace embedded in the token.

The outcome

The root cause is identified directly from the decoded token without needing to reproduce the issue in a test environment. The fix is implemented based on the specific error information, and the response time for the support case drops from days to hours.

The problem

A privacy team needs to audit the cookies set by their website before a compliance review. They need to verify that all cookies have appropriate security attributes (Secure, HttpOnly, SameSite), that tracking cookies are not set before consent, and that cookie lifetimes are reasonable.

The solution

The team extracts Set-Cookie headers from their website's responses using browser DevTools, then pastes them into the Cookie Inspector. The tool parses each cookie, displays all attributes in a structured table, and flags security issues: a session cookie missing the Secure flag, a tracking cookie without SameSite=Lax, and an analytics cookie with a 10-year expiry.

The outcome

The audit produces a clear list of cookies that need remediation, with specific attribute changes recommended for each. The development team addresses the issues before the compliance review, and the audit report documents the before-and-after state of each cookie.

The problem

After deploying a new Consent Management Platform (CMP), the ad operations team needs to verify that consent strings are being generated correctly. Users who accept all purposes should have all purpose bits set, users who reject everything should have all bits cleared, and vendor consent should match the CMP's vendor list configuration.

The solution

The team captures consent strings from the euconsent-v2 cookie after performing different consent actions (accept all, reject all, selective consent). Each string is decoded using the TCF String Decoder, which shows the metadata (CMP ID, version, vendor list version), purpose consent bits, and vendor consent arrays. The team verifies that each consent action produces the expected bit pattern.

The outcome

A configuration error is discovered: the CMP was not setting Purpose 7 (measure ad performance) even when users accepted all purposes. The CMP vendor corrects the configuration, and the team re-tests to confirm all purposes are now encoded correctly before the CMP goes live on all pages.

The problem

An analytics dashboard shows a spike in video playback errors on a specific segment of traffic, but the segment is defined only by a user-agent string pattern. The QA team needs to identify what device class, OS, and browser this represents to reproduce the issue.

The solution

The team pastes the user-agent string from the error logs into the User Agent parser. The tool identifies the traffic as coming from a specific version of an in-app WebView on Android 12, running a Chromium-based engine. This reveals that the issue is likely related to a known WebView bug in that Android version.

The outcome

The team reproduces the issue on an Android 12 test device, confirms the WebView bug, and implements a workaround in the video player code. The fix is targeted to the specific user-agent pattern, avoiding unnecessary changes for other devices.

The problem

A video engineering team is testing a new HLS player implementation and needs sample HLS streams to validate playback, seeking, and quality switching. Creating HLS packages typically requires server-side encoding infrastructure that is not available in the test environment.

The solution

The team uses the MP4 to HLS Converter to upload sample MP4 files and convert them to HLS format directly in the browser. The tool generates M3U8 playlists and TS segments that can be served locally for player testing. Different segment lengths are tested to validate seeking behavior with short and long segments.

The outcome

The player is validated against multiple HLS configurations without any server-side setup. The team identifies a seeking bug with short segments (2-second) that does not occur with longer segments (10-second), leading to a targeted fix in the player's segment boundary handling.

The problem

An operations team is migrating configuration data from a legacy system to a new platform. The exported data contains HTML entities, inconsistent whitespace, Windows-style line breaks, and encoded characters that the new system does not accept.

The solution

The team pastes the exported data into the Find and Replace tool and applies a sequence of preset rules: decode HTML entities, normalize line breaks (CRLF to LF), collapse multiple spaces into single spaces, and strip remaining HTML tags. The live preview shows the transformations before they are applied, and the match count confirms the expected number of replacements.

The outcome

The data is cleaned in a single session instead of writing custom scripts for each transformation. The exported clean data imports successfully into the new system on the first attempt, saving the team a full day of manual cleanup and debugging.

The problem

A growing engineering team needs to onboard new developers quickly, but the project's directory structure is complex and undocumented. New team members spend their first days navigating the codebase to understand where different modules, configurations, and tests live.

The solution

A senior developer pastes the project's file path listing into the Directory Tree Generator, which produces a clean, visual tree showing the complete project hierarchy. The tree is added to the project's README with annotations explaining the purpose of each major directory.

The outcome

New developers can reference the documented structure from day one, reducing onboarding questions and helping them locate relevant code faster. The tree is updated periodically as the project structure evolves, maintaining an accurate map of the codebase.