# SEO Meta & Schema Extractor (`blazing_stake/seo-meta-extractor`) Actor Extract SEO metadata, Open Graph, Twitter Cards, JSON-LD schema, canonical, hreflang and headings from any list of URLs. Fast HTTP-based crawler. - **URL**: https://apify.com/blazing\_stake/seo-meta-extractor.md - **Developed by:** [Mehmet Kut](https://apify.com/blazing_stake) (community) - **Categories:** SEO tools, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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 ## SEO Meta & Schema Extractor Extract complete **SEO metadata** from any list of URLs — fast, cheap, and at scale. This Actor pulls titles, meta descriptions, Open Graph tags, Twitter Cards, JSON-LD structured data, canonical URLs, hreflang alternates, headings, and image alt coverage from every page you give it. Built on an HTTP crawler (no headless browser), so it's **10× faster and cheaper** than browser-based scrapers. ### 🎯 What it does For each URL, it extracts: - **Title** + character length (SEO length audit) - **Meta description** + character length - **Canonical URL**, robots, viewport, charset, `lang` - **Open Graph** tags (`og:title`, `og:image`, `og:type`, …) - **Twitter Card** tags - **hreflang** alternates (international SEO) - **JSON-LD** structured data (schema.org) + detected `@type`s - **Headings** (H1, H2, H3) - **Image stats** — total images + alt-text coverage % - **Word count** ### 💡 Use cases - **Technical SEO audits** — spot missing titles, thin descriptions, absent canonicals - **Competitor research** — see how competitors structure their metadata & schema - **Content inventory** — catalog metadata across an entire site - **Structured-data validation** — extract and check JSON-LD at scale ### 📥 Input | Field | Type | Description | |-------|------|-------------| | `startUrls` | array | List of page URLs to extract from | | `maxConcurrency` | integer | Parallel requests (default 20) | | `useProxy` | boolean | Route via Apify Proxy (default true) | | `extractJsonLd` | boolean | Include JSON-LD structured data | | `extractHeadings` | boolean | Include H1–H3 headings | #### Example input ```json { "startUrls": ["https://example.com", "https://example.com/about"], "maxConcurrency": 20, "useProxy": true } ```` ### 📤 Output Each result is a structured JSON object: ```json { "url": "https://example.com", "title": "Example Domain", "titleLength": 14, "description": "...", "descriptionLength": 143, "canonical": "https://example.com", "openGraph": { "og:title": "...", "og:image": "..." }, "twitter": { "twitter:card": "summary" }, "hreflang": [{ "lang": "en", "href": "..." }], "jsonLdTypes": ["Organization", "WebSite"], "images": { "total": 79, "withAlt": 79, "altCoveragePct": 100 }, "wordCount": 1919 } ``` Export to JSON, CSV, Excel, or feed directly into your own tools via the Apify API. ### ⚡ Performance HTTP-based crawling processes pages in ~0.5s each. A 1,000-URL batch finishes in minutes, not hours. ### 🔗 Tip Pair this with the **Sitemap URL Extractor** to first pull every URL of a site, then feed them here for a full-site SEO audit. # Actor input Schema ## `startUrls` (type: `array`): List of page URLs to extract SEO metadata from. Accepts plain URL strings or objects with a 'url' field. ## `maxConcurrency` (type: `integer`): Maximum number of pages fetched in parallel. Higher is faster but may get rate-limited by target sites. ## `useProxy` (type: `boolean`): Route requests through Apify Proxy to avoid IP blocking. Recommended for large batches. ## `extractJsonLd` (type: `boolean`): Parse and include schema.org JSON-LD structured data blocks found on each page. ## `extractHeadings` (type: `boolean`): Include the text of H1, H2 and H3 headings (useful for content/SEO audits). ## Actor input object example ```json { "startUrls": [ "https://example.com/page-1", "https://example.com/page-2" ], "maxConcurrency": 20, "useProxy": true, "extractJsonLd": true, "extractHeadings": 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 = { "startUrls": [ "https://apify.com", "https://www.wikipedia.org" ] }; // Run the Actor and wait for it to finish const run = await client.actor("blazing_stake/seo-meta-extractor").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 = { "startUrls": [ "https://apify.com", "https://www.wikipedia.org", ] } # Run the Actor and wait for it to finish run = client.actor("blazing_stake/seo-meta-extractor").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 '{ "startUrls": [ "https://apify.com", "https://www.wikipedia.org" ] }' | apify call blazing_stake/seo-meta-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=blazing_stake/seo-meta-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SEO Meta & Schema Extractor", "description": "Extract SEO metadata, Open Graph, Twitter Cards, JSON-LD schema, canonical, hreflang and headings from any list of URLs. Fast HTTP-based crawler.", "version": "0.1", "x-build-id": "0YTPqQel0ppl0yuUd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/blazing_stake~seo-meta-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-blazing_stake-seo-meta-extractor", "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/blazing_stake~seo-meta-extractor/runs": { "post": { "operationId": "runs-sync-blazing_stake-seo-meta-extractor", "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/blazing_stake~seo-meta-extractor/run-sync": { "post": { "operationId": "run-sync-blazing_stake-seo-meta-extractor", "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": [ "startUrls" ], "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "List of page URLs to extract SEO metadata from. Accepts plain URL strings or objects with a 'url' field.", "items": { "type": "string" } }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of pages fetched in parallel. Higher is faster but may get rate-limited by target sites.", "default": 20 }, "useProxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Route requests through Apify Proxy to avoid IP blocking. Recommended for large batches.", "default": true }, "extractJsonLd": { "title": "Extract JSON-LD structured data", "type": "boolean", "description": "Parse and include schema.org JSON-LD structured data blocks found on each page.", "default": true }, "extractHeadings": { "title": "Extract headings (H1-H3)", "type": "boolean", "description": "Include the text of H1, H2 and H3 headings (useful for content/SEO audits).", "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 } } } } } } } } } } ```