Schema Markup Extractor - JSON-LD SEO Data

Extract Schema.org JSON-LD structured data from web pages and export schema types, structured data nodes, block counts and parse errors. Use it for SEO audits, rich result checks, content QA, ecommerce validation and structured data monitoring.

What is the Schema Markup Extractor?

Schema markup helps search engines understand a page. It can describe products, articles, organizations, breadcrumbs, FAQs, reviews, jobs, events, recipes and many other entities. When schema is missing or broken, rich results and search understanding can suffer.

This actor fetches each page, finds application/ld+json blocks, parses them as JSON and returns the extracted nodes in a structured dataset. It also summarizes the schema types found on each page, which makes bulk audits easier.

Common use cases

Use this actor to audit structured data across a website, verify Product schema on ecommerce pages, check Article schema on blogs, inspect Organization and WebSite schema, monitor job posting markup, validate FAQ or Breadcrumb schema, or compare competitor structured data.

SEO teams can find pages with missing JSON-LD. Ecommerce teams can confirm product data is exposed. Agencies can export schema evidence for clients. Developers can test whether deploys changed structured data.

Input

Provide one or more page URLs.

{
  "urls": [
    "https://apify.com"
  ]
}

Output

Each dataset item represents one page.

{
  "input": "https://apify.com",
  "url": "https://apify.com",
  "final_url": "https://apify.com/",
  "domain": "apify.com",
  "status_code": 200,
  "json_ld_block_count": 2,
  "schema_node_count": 5,
  "schema_types": ["Organization", "WebSite"],
  "schema_nodes": [
    {
      "@context": "https://schema.org",
      "@type": "Organization",
      "name": "Apify"
    }
  ],
  "parse_errors": [],
  "error": null
}

Output fields

• json_ld_block_count - number of JSON-LD script blocks.
• schema_node_count - number of extracted schema nodes.
• schema_types - unique @type values found.
• schema_nodes - parsed structured data objects.
• parse_errors - JSON parse errors from malformed blocks.
• final_url, domain, status_code - request metadata.
• error - request error, if one occurred.

Why use this actor?

Structured data is easiest to inspect programmatically. This actor gives you a clean dataset instead of requiring manual browser checks. It is fast because it reads HTML directly, and it integrates with Apify schedules, webhooks, exports and API workflows.

Example workflows

Collect product URLs from a sitemap, extract schema, and filter pages missing Product or Offer. Audit article URLs and confirm Article or NewsArticle schema exists. Run it before and after a release to catch accidental template changes. Build a competitor dataset showing which schema types are used in your niche.

For job boards, check whether JobPosting schema appears on job detail pages. For local businesses, inspect LocalBusiness, Organization and BreadcrumbList markup. For publishers, monitor article templates and author pages. For ecommerce teams, export Product, Offer, AggregateRating and Review nodes to confirm that rich-result inputs are present.

Integration ideas

Use the actor after a sitemap extractor to audit a sample of important URLs. Send schema_types into a spreadsheet and filter pages without required types. Store historical runs and alert when schema disappears from a template. Combine this actor with the Meta Tags Extractor to create a broader SEO health dataset for each page.

Agencies can use the output as evidence in technical SEO reports. Developers can run it during release QA to make sure structured data blocks are still valid JSON. Data teams can use schema nodes as a lightweight source of product, organization or article metadata when the site exposes it publicly.

Best practices

Use actual public URLs, not editor previews. Inspect parse_errors for malformed JSON-LD. Remember that this actor extracts markup; it does not replace Google's Rich Results Test or validate every schema requirement. For critical pages, use this output as the bulk audit layer and then validate representative samples with a specialized validator.

Audit by template. One homepage, one product page, one category page and one article page can each expose different schema. For bulk checks, group results by schema_types and investigate missing or unexpected groups. If a site injects JSON-LD only after JavaScript execution, direct HTML extraction may not see it; those sites need a browser-rendered workflow.

Data quality notes

The actor extracts JSON-LD script blocks and flattens top-level arrays and @graph nodes. It preserves parsed nodes so advanced users can inspect all fields. It reports malformed JSON in parse_errors instead of failing the entire run. Schema types are deduplicated and sorted to make filtering easier.

Commercial value

Structured data is one of the clearest technical SEO checks to productize. It is visible, measurable and tied to rich search features. This actor helps agencies and site owners audit many pages quickly, document gaps, and verify fixes after template changes without manually opening each page in a browser.

FAQ

Does it extract microdata?

This version focuses on JSON-LD, the most common modern structured data format.

Does it render JavaScript?

No. It reads server-returned HTML. Important SEO JSON-LD should usually be present there.

Can I audit ecommerce pages?

Yes. Product, Offer, Review and Breadcrumb schema are common use cases.

Can I export schema nodes?

Yes. The full parsed nodes are included in the dataset.

You might also like

• Meta Tags Extractor - extract titles, descriptions and Open Graph tags.
• Sitemap URL Extractor - collect URLs for schema audits.
• Website Status Checker - check page availability before audits.

Keywords

schema markup extractor, JSON-LD scraper, structured data scraper, Schema.org extractor, SEO schema audit, rich results data, Product schema checker, Article schema scraper, Apify schema tool.