# Ad Pixel & Marketing Tracker Auditor (`express_kingfisher/ad-pixel-tracker-auditor`) Actor Audit any website for advertising pixels, analytics trackers, and marketing tools. Detect Meta, TikTok, Google Ads, GTM, GA4, LinkedIn, and more. - **URL**: https://apify.com/express\_kingfisher/ad-pixel-tracker-auditor.md - **Developed by:** [Prince Raj](https://apify.com/express_kingfisher) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Ad Pixel & Marketing Tracker Auditor Audit advertising pixels, tracking scripts, and marketing technology stacks across websites. Built for marketing agencies, media buyers, and compliance teams who need to verify tracking implementations at scale. ### What It Extracts - **Ad pixels**: Meta (Facebook), TikTok, Google Ads, LinkedIn, Pinterest, Reddit, Quora - **Analytics**: GA4, Google Tag Manager, Hotjar, Microsoft Clarity, Mixpanel, Amplitude - **Consent signals**: Cookie banner detection, consent mode indicators - **Implementation issues**: Duplicate pixels, missing configurations - **Marketing stack summary**: Complete overview of tracking technologies ### Why It's Better Generates client-ready audit reports with actionable recommendations. Identifies misconfigured trackers, duplicate implementations, and compliance gaps that manual inspection misses. ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `urls` | string[] | required | URLs or domains to audit | | `maxPagesPerDomain` | integer | 5 | Pages to scan per domain (1-20) | | `checkConsent` | boolean | true | Check for consent banner/mode | | `includeRecommendations` | boolean | true | Generate implementation recommendations | ### Output Example ```json { "domain": "example.com", "statusCode": 200, "pixels": [ { "name": "Meta Pixel", "id": "1234567890", "platform": "facebook", "category": "advertising" }, { "name": "Google Ads", "id": "AW-123456789", "platform": "google", "category": "advertising" }, { "name": "GA4", "id": "G-XXXXXXXXXX", "platform": "google", "category": "analytics" } ], "hasConsentBanner": true, "hasConsentMode": false, "issues": ["Duplicate Meta pixel detected", "Missing consent mode"], "recommendations": ["Implement Google Consent Mode v2", "Remove duplicate pixel"], "scrapedAt": "2025-01-15T10:30:00Z" } ```` ### Use Cases - **Client onboarding audits**: Quickly assess a new client's tracking setup - **Compliance checks**: Verify GDPR/CCPA consent implementation - **Competitor analysis**: See what tracking competitors use - **Pre-launch audits**: Verify pixel firing before campaign launch - **Troubleshooting**: Identify why conversion tracking isn't working ### PPE Pricing | Event | Description | Suggested Price | |-------|-------------|-----------------| | `domain-audited` | Full domain audit completed | $0.01 | | `page-audited` | Additional page scanned | $0.003 | ### Limitations - Only detects client-side pixels (server-side tracking not visible) - Consent banner detection is heuristic-based - Cannot verify pixel firing accuracy (only presence) ### Legal/Ethical Use This actor inspects publicly visible page source code. It does not access private data or bypass authentication. Users are responsible for compliance with applicable laws. ### Local Run ```bash cd actors/ad-pixel-tracker-auditor apify run --input-file .actor/sample_input.json ``` ### Deploy ```bash cd actors/ad-pixel-tracker-auditor apify push ``` ### FAQ **Q: Can it detect pixels loaded via GTM?** A: It detects the GTM container. Pixels loaded dynamically via GTM may not be individually identified. **Q: Does it check for consent compliance?** A: Yes, it checks for cookie banners and consent mode when `checkConsent` is enabled. **Q: How accurate is duplicate pixel detection?** A: High accuracy for standard implementations. Custom setups may need manual verification. ### Tags ad pixel audit, marketing tracker, Meta pixel checker, Google Ads tag, GDPR compliance, consent mode, GTM audit, marketing stack analysis, conversion tracking audit # Actor input Schema ## `urls` (type: `array`): List of URLs or domains to scan for tracking pixels and marketing tools. ## `maxPagesPerDomain` (type: `integer`): Number of pages to crawl per domain for comprehensive detection. ## `checkConsent` (type: `boolean`): Detect cookie consent banners and GDPR compliance signals. ## `includeRecommendations` (type: `boolean`): Generate implementation recommendations and fix suggestions. ## Actor input object example ```json { "urls": [ "https://apify.com" ], "maxPagesPerDomain": 3, "checkConsent": false, "includeRecommendations": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "urls": [ "https://apify.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("express_kingfisher/ad-pixel-tracker-auditor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "urls": ["https://apify.com"] } # Run the Actor and wait for it to finish run = client.actor("express_kingfisher/ad-pixel-tracker-auditor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://apify.com" ] }' | apify call express_kingfisher/ad-pixel-tracker-auditor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=express_kingfisher/ad-pixel-tracker-auditor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ad Pixel & Marketing Tracker Auditor", "description": "Audit any website for advertising pixels, analytics trackers, and marketing tools. Detect Meta, TikTok, Google Ads, GTM, GA4, LinkedIn, and more.", "version": "1.0", "x-build-id": "9mN47UJmYJ3KwnoL7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/express_kingfisher~ad-pixel-tracker-auditor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-express_kingfisher-ad-pixel-tracker-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/express_kingfisher~ad-pixel-tracker-auditor/runs": { "post": { "operationId": "runs-sync-express_kingfisher-ad-pixel-tracker-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/express_kingfisher~ad-pixel-tracker-auditor/run-sync": { "post": { "operationId": "run-sync-express_kingfisher-ad-pixel-tracker-auditor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "urls" ], "properties": { "urls": { "title": "URLs to audit", "maxItems": 50, "type": "array", "description": "List of URLs or domains to scan for tracking pixels and marketing tools.", "items": { "type": "string" } }, "maxPagesPerDomain": { "title": "Max pages per domain", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of pages to crawl per domain for comprehensive detection.", "default": 3 }, "checkConsent": { "title": "Check consent implementation", "type": "boolean", "description": "Detect cookie consent banners and GDPR compliance signals.", "default": false }, "includeRecommendations": { "title": "Include recommendations", "type": "boolean", "description": "Generate implementation recommendations and fix suggestions.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```