# Meta Tags Extractor (`automationagents/web-meta-tags`) Actor Extract OpenGraph, Twitter Card, and SEO meta tags from any URL. - **URL**: https://apify.com/automationagents/web-meta-tags.md - **Developed by:** [Alex Jordan](https://apify.com/automationagents) (community) - **Categories:** Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 successful api calls This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ### What does Meta Tags Extractor do? **Meta Tags Extractor** pulls all SEO and social meta tags from any URL — OpenGraph (`og:title`, `og:image`, `og:description`), Twitter Card tags, canonical URL, favicon, and standard HTML meta tags — in a single API call. No browser needed, no setup required. Built on the **Apify platform**, this Actor returns results in seconds and integrates with Apify's scheduling, webhooks, and 1,500+ tools via Zapier and Make. ### Why use Meta Tags Extractor? - **SEO auditing** — Bulk-check meta tags across hundreds of pages to find missing or duplicate tags - **Social preview validation** — Verify that OpenGraph and Twitter Card images and titles are set correctly before publishing - **Competitor research** — See exactly how competitors optimise their pages for search and social sharing - **Content enrichment** — Enrich a list of URLs with title, description, and image data for CRM or data pipelines - **Link preview generation** — Build link preview cards from any URL without loading the page in a browser ### How to use Meta Tags Extractor 1. Click **Try for free** on this Actor's page 2. Enter the **URL** you want to analyse (e.g. `https://stripe.com`) 3. Click **Start** and wait a few seconds 4. Download your results from the **Output** tab in JSON, CSV, or Excel ### Input | Field | Type | Required | Description | |---|---|---|---| | `url` | string | ✅ | Page URL to extract meta tags from | | `cache` | boolean | ❌ | Use cached result if available (default true) | **Example input:** ```json { "url": "https://stripe.com" } ```` ### Output **Example output:** ```json { "title": "Stripe | Financial Infrastructure for the Internet", "description": "Millions of businesses use Stripe...", "og": { "og:title": "Stripe", "og:description": "Millions of businesses...", "og:image": "https://stripe.com/img/v3/home/social.png", "og:url": "https://stripe.com" }, "twitter": { "twitter:card": "summary_large_image", "twitter:title": "Stripe", "twitter:image": "https://stripe.com/img/v3/home/social.png" }, "favicon": "https://stripe.com/favicon.ico", "canonical": "https://stripe.com/", "meta": { "cache_hit": false, "execution_time_ms": 420 } } ``` You can download the dataset in various formats such as JSON, HTML, CSV, or Excel. ### Data fields | Field | Type | Description | |---|---|---| | `title` | string | HTML page title | | `description` | string | Meta description tag | | `og` | object | All OpenGraph tags (og:title, og:image, og:url, etc.) | | `twitter` | object | All Twitter Card tags | | `favicon` | string | Favicon URL | | `canonical` | string | Canonical URL declared on the page | ### Pricing / Cost estimation **$0.02 per successful API call** on Apify. - 1,000 successful Apify runs = $20.00 ### FAQ & Support **Is this legal?** This Actor reads publicly available HTML meta tags — the same data any search engine bot reads. **Known limitations:** JavaScript-rendered meta tags (added after page load) may not be captured. For JS-heavy pages, use the **Web Page Scraper** Actor. **Need help?** Open an issue in the Issues tab or contact the support team for custom solutions. # Actor input Schema ## `url` (type: `string`): Page URL to extract meta tags from ## `cache` (type: `boolean`): Use cached result if available ## Actor input object example ```json { "url": "https://stripe.com", "cache": true } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "url": "https://stripe.com", "cache": true }; // Run the Actor and wait for it to finish const run = await client.actor("automationagents/web-meta-tags").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "url": "https://stripe.com", "cache": True, } # Run the Actor and wait for it to finish run = client.actor("automationagents/web-meta-tags").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 '{ "url": "https://stripe.com", "cache": true }' | apify call automationagents/web-meta-tags --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automationagents/web-meta-tags", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Meta Tags Extractor", "description": "Extract OpenGraph, Twitter Card, and SEO meta tags from any URL.", "version": "0.0", "x-build-id": "iuToDKGvu8FTxogHy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automationagents~web-meta-tags/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automationagents-web-meta-tags", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automationagents~web-meta-tags/runs": { "post": { "operationId": "runs-sync-automationagents-web-meta-tags", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automationagents~web-meta-tags/run-sync": { "post": { "operationId": "run-sync-automationagents-web-meta-tags", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "url" ], "properties": { "url": { "title": "Url", "type": "string", "description": "Page URL to extract meta tags from" }, "cache": { "title": "Cache", "type": "boolean", "description": "Use cached result if available" } } }, "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 } } } } } } } } } } ```