# Structured Data Extractor - JSON-LD, OpenGraph, Microdata (`gratifying_graph/structured-data-extractor`) Actor Extract every piece of structured data from any URL: JSON-LD blocks by schema.org type, OpenGraph and Twitter Card tags, microdata items, canonical and meta basics. Batch over URL lists or call synchronously from AI agents. - **URL**: https://apify.com/gratifying\_graph/structured-data-extractor.md - **Developed by:** [Jimmy A](https://apify.com/gratifying_graph) (community) - **Categories:** Developer tools, AI - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 url extracteds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Structured Data Extractor - JSON-LD, OpenGraph, Twitter Cards, Microdata Extract **all the structured data** from any URL in one call: every JSON-LD block with its schema.org types, OpenGraph and Twitter Card tags, canonical URL, meta basics, and microdata items. Batch over URL lists, or call it synchronously from AI agents and SEO tools. ### What you get per URL ```json { "url": "https://example.com/article", "status": 200, "title": "Page title", "metaDescription": "...", "canonical": "https://example.com/article", "jsonLd": [ { "@context": "https://schema.org", "@type": "Article", "...": "..." } ], "jsonLdTypes": ["Article"], "openGraph": { "title": "...", "image": "...", "type": "article" }, "twitter": { "card": "summary_large_image" }, "microdataItems": [], "errors": [] } ```` Malformed JSON-LD is reported in `errors` instead of being silently dropped - useful for SEO audits. Unreachable URLs produce an error record without killing the batch. ### Use cases - **SEO audits**: verify structured data across a site (pair with a sitemap extractor for full coverage) - **Rich-result debugging**: see exactly what crawlers see, including parse errors - **Content pipelines**: pull clean Article/Product/Recipe/Event objects without writing per-site scrapers - **AI agents**: a fetch-and-understand primitive - one call returns the machine-readable summary of any page - **Competitive research**: what schema types and OG strategies competitors deploy ### API / Standby mode ``` GET /?url=https://example.com/page ``` Returns the extraction JSON synchronously. Works as a tool for agent frameworks that support Apify actors. ### Pricing | Event | Price | |---|---| | Actor start | $0.0001 | | Per URL extracted | $0.002 | | API call (standby) | $0.01 | A 1,000-page site audit costs $2. ### FAQ **Does it render JavaScript?** No - it reads the served HTML, which is where structured data lives for any site that wants search engines to see it. That keeps it fast and cheap. JS-only SPAs may show fewer blocks. **What about pages behind bot protection?** You get an honest error record with the HTTP status. No retries through gray-zone proxies. **Nested @graph and arrays?** Handled - types are collected recursively from @graph structures and type arrays. # Actor input Schema ## `urls` (type: `array`): Pages to extract structured data from. ## `includeMicrodata` (type: `boolean`): Also walk itemscope/itemprop microdata (slower on huge pages; JSON-LD and meta tags are always extracted). ## `maxUrls` (type: `integer`): Cap on the number of URLs processed in one run. ## Actor input object example ```json { "urls": [ "https://blog.apify.com/" ], "includeMicrodata": true, "maxUrls": 1000 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "urls": [ "https://blog.apify.com/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("gratifying_graph/structured-data-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "urls": ["https://blog.apify.com/"] } # Run the Actor and wait for it to finish run = client.actor("gratifying_graph/structured-data-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 '{ "urls": [ "https://blog.apify.com/" ] }' | apify call gratifying_graph/structured-data-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gratifying_graph/structured-data-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Structured Data Extractor - JSON-LD, OpenGraph, Microdata", "description": "Extract every piece of structured data from any URL: JSON-LD blocks by schema.org type, OpenGraph and Twitter Card tags, microdata items, canonical and meta basics. Batch over URL lists or call synchronously from AI agents.", "version": "1.0", "x-build-id": "Veof3uKExQyyPQy6S" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gratifying_graph~structured-data-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gratifying_graph-structured-data-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/gratifying_graph~structured-data-extractor/runs": { "post": { "operationId": "runs-sync-gratifying_graph-structured-data-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/gratifying_graph~structured-data-extractor/run-sync": { "post": { "operationId": "run-sync-gratifying_graph-structured-data-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "urls" ], "properties": { "urls": { "title": "URLs", "type": "array", "description": "Pages to extract structured data from.", "items": { "type": "string" } }, "includeMicrodata": { "title": "Include microdata", "type": "boolean", "description": "Also walk itemscope/itemprop microdata (slower on huge pages; JSON-LD and meta tags are always extracted).", "default": true }, "maxUrls": { "title": "Max URLs", "minimum": 1, "type": "integer", "description": "Cap on the number of URLs processed in one run.", "default": 1000 } } }, "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 } } } } } } } } } } ```