# Meta Ad Library Page Resolver (`adside/meta-ad-library-page-resolver`) Actor Find a brand's Meta & Facebook advertiser pages in the Meta Ad Library by name. Get the page ID, profile, likes, verification & Instagram handle. - **URL**: https://apify.com/adside/meta-ad-library-page-resolver.md - **Developed by:** [Adside](https://apify.com/adside) (community) - **Categories:** Social media, AI, Lead generation - **Stats:** 7 total users, 6 monthly users, 100.0% 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 ## Meta Ad Library Page Resolver Resolves a brand/company name to candidate Meta/Facebook advertiser pages from Meta Ad Library typeahead. This actor is intended to replace `scrapeio~facebook-ad-library-suggestions` with a narrower, cheaper, and more auditable resolver. It does not scrape ads. It only resolves candidate advertiser pages so the app can decide which page ID to pass to the Meta ads scraper. ### Input ```json { "brand_name": "Apple", "website_domain": "apple.com", "country": "ALL", "limit": 10 } ```` ### Output Dataset rows use the same core contract as the current actor: ```json { "adlibrary_url": "6629105669", "brand_name": "Apple", "category": "Product/service", "avatar_url": "https://...", "likes": 16100000, "verification": "BLUE_VERIFIED", "page_alias": "apple", "ig_username": "apple", "ig_followers": 36600000, "ig_verification": true, "profile_url": "https://www.facebook.com/apple", "score": 95, "source": "typeahead" } ``` ### Current Implementation Notes The HAR captures showed the relevant endpoint: ```text POST https://www.facebook.com/api/graphql/ fb_api_req_friendly_name=useAdLibraryTypeaheadSuggestionDataSourceQuery doc_id=9755915494515334 variables={ "queryString": "Apple", "isMobile": false, "country": "ALL", "adType": "ALL" } ``` The response already contains the page ID and all useful identity fields under: ```text data.ad_library_main.typeahead_suggestions.page_results[] ``` The actor uses `impit` for browser-impersonated direct HTTP requests and does not launch a browser. Recommended architecture: - Actor: extraction and normalization only. - Adside app: final matching with deterministic rules and LLM fallback when candidates are ambiguous. ### Country Behavior Observed For `Apple`, `ALL`, `FR`, and `US` return effectively the same top candidates: 1. Apple TV 2. Apple 3. Apple Music / other Apple-adjacent pages The `country` parameter is still real and present in the GraphQL variables. Keep it configurable, but prefer `ALL` for global company resolution unless a workspace has an explicit market. # Actor input Schema ## `brand_name` (type: `string`): Brand, company, or product name to resolve in Meta Ad Library. ## `website_domain` (type: `string`): Optional canonical website domain used for scoring candidates, e.g. apple.com. ## `country` (type: `string`): Meta Ad Library country selector. Use ALL for global or ISO-2 country code such as US, FR, IN. ## `limit` (type: `integer`): Maximum number of candidates to return. ## Actor input object example ```json { "country": "ALL", "limit": 10 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("adside/meta-ad-library-page-resolver").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 = {} # Run the Actor and wait for it to finish run = client.actor("adside/meta-ad-library-page-resolver").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 '{}' | apify call adside/meta-ad-library-page-resolver --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=adside/meta-ad-library-page-resolver", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Meta Ad Library Page Resolver", "description": "Find a brand's Meta & Facebook advertiser pages in the Meta Ad Library by name. Get the page ID, profile, likes, verification & Instagram handle.", "version": "0.1", "x-build-id": "U3WfAPXp44lzU2Lvu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/adside~meta-ad-library-page-resolver/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-adside-meta-ad-library-page-resolver", "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/adside~meta-ad-library-page-resolver/runs": { "post": { "operationId": "runs-sync-adside-meta-ad-library-page-resolver", "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/adside~meta-ad-library-page-resolver/run-sync": { "post": { "operationId": "run-sync-adside-meta-ad-library-page-resolver", "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": [ "brand_name" ], "properties": { "brand_name": { "title": "Brand name", "type": "string", "description": "Brand, company, or product name to resolve in Meta Ad Library." }, "website_domain": { "title": "Website domain", "type": "string", "description": "Optional canonical website domain used for scoring candidates, e.g. apple.com." }, "country": { "title": "Country", "type": "string", "description": "Meta Ad Library country selector. Use ALL for global or ISO-2 country code such as US, FR, IN.", "default": "ALL" }, "limit": { "title": "Limit", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum number of candidates to return.", "default": 10 } } }, "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 } } } } } } } } } } ```