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Schema Markup Scraper & SEO Auditor

Extract JSON-LD, Microdata, RDFa, Open Graph & Twitter Cards. Runs a 0-100 SEO audit — checks canonical, hreflang, headings, image alt, EEAT author signals. Detects 80+ schema.org types including LocalBusiness with NAP, geo coordinates, and Google Place IDs.

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from $3.00 / 1,000 results

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Richard Feng

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11 days ago

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Schema Markup Scraper & SEO Analyzer

Extract structured data, metadata, and SEO signals from any web page. Built for technical SEO auditing, local business intelligence, content aggregation, and competitive analysis.

What it does

This scraper visits one or more URLs and extracts everything a search engine sees: structured data (JSON-LD, Microdata, RDFa), social meta tags (Open Graph, Twitter Cards), and dozens of SEO signals. It then runs an automated audit and returns a 0-100 SEO score with actionable issues — aligned with Google's 2025 ranking signals and EEAT guidelines.

Key capabilities

Structured data extraction

JSON-LD — Parses all <script type="application/ld+json"> blocks, including nested @graph structures
Microdata — Extracts itemscope/itemprop schema.org markup with full nesting support
RDFa (opt-in) — Parses typeof/property/vocab attributes with schema.org vocabulary resolution
Schema type detection — Identifies all schema.org types present (Product, Article, LocalBusiness, BreadcrumbList, etc.)

Open Graph — All og:* properties (title, description, image, type, locale, etc.)
Twitter Cards — All twitter:* properties with special handling for summary_large_image
Dublin Core — DC.* and DCTerms.* academic/institutional metadata
Standard meta tags — viewport, description, keywords, robots, theme-color, and all others

SEO analysis

Canonical URL — Detects <link rel="canonical">
Robots meta — Extracts directives for robots, googlebot, bingbot, etc.
Heading hierarchy — Maps H1–H6 structure, counts H1 tags, detects skipped levels
Image alt text audit — Counts images with/without alt attributes, calculates coverage percentage
Viewport & charset — Verifies mobile-first indexing prerequisites
SEO score (0-100) — Automated audit checking 15 Google ranking signals with error/warning/info severity

International SEO

Hreflang tags — Extracts all <link rel="alternate" hreflang="..."> with built-in validation:
- Flags missing x-default fallback
- Validates ISO 639-1 language codes (catches common mistakes like en-UK → should be en-GB)
- Detects missing self-referencing tags
Language detection — <html lang>, <meta http-equiv="content-language">, og:locale

EEAT & author signals

Author extraction — Pulls author info from JSON-LD (Person type with sameAs links), <meta name="author">, and <a rel="author">
Article metadata — datePublished, dateModified, headline, wordCount, publisher from Article/NewsArticle/BlogPosting schema

Local / Geo SEO

LocalBusiness extraction — Detects 80+ schema.org LocalBusiness subtypes (Restaurant, Hotel, Dentist, Store, etc.) and extracts NAP, geo coordinates, opening hours, price range
NAP (Name/Address/Phone) — From any Organization or LocalBusiness schema
Geo meta tags — geo.region, geo.placename, geo.position, ICBM
Google Maps references — Embedded map iframes, Place IDs, CID numbers
hCard/vCard (opt-in) — .vcard/.h-card microformat contact data

Extracts BreadcrumbList schema items with position, name, and URL
Validates sequential positions and flags relative URLs

Input parameters

Parameter	Type	Default	Description
`startUrls`	Array	(required)	URLs to scrape
`proxy`	Object	Apify Proxy	Proxy configuration
`maxRequestsPerCrawl`	Integer	`100`	Maximum pages to scrape (1–100,000)
`maxConcurrency`	Integer	`10`	Parallel pages (1–100)
`extractMetaTags`	Boolean	`true`	Extract all meta tags
`extractSeoAnalysis`	Boolean	`true`	SEO signals: canonical, hreflang, robots, headings, author, images, breadcrumbs, Dublin Core, viewport, charset
`extractGeoData`	Boolean	`true`	Geo tags, LocalBusiness, NAP, Google Maps references
`computeSeoScore`	Boolean	`true`	Run SEO audit (0-100 score + issues list)
`extractRdfa`	Boolean	`false`	RDFa structured data (opt-in)
`extractHCard`	Boolean	`false`	hCard/vCard microformats (opt-in)

Output fields

Each scraped page produces a JSON object with these fields:

Core metadata

Field	Type	Description
`url`	String	Final URL after redirects
`title`	String	Page `<title>` content
`icon`	String	Favicon/apple-touch-icon URL
`linkedData`	Array	JSON-LD structured data blocks
`microdata`	Array	Microdata (schema.org) items
`openGraph`	Object	Open Graph properties
`twitterCard`	Object	Twitter Card properties
`metaTags`	Object	All other meta tags

SEO analysis (when `extractSeoAnalysis` is enabled)

Field	Type	Description
`canonical`	String/null	Canonical URL
`robotsMeta`	Object	Robots directives (`{ robots: "index, follow", googlebot: "noarchive" }`)
`hreflang`	Object	`{ tags: [{lang, url}], hasXDefault: bool, issues: string[] }`
`language`	Object	`{ htmlLang, contentLanguage, ogLocale }`
`dublinCore`	Object	Dublin Core metadata
`viewport`	String/null	Viewport meta tag content
`charset`	String/null	Character encoding
`headings`	Object	`{ headings: [{level, text}], h1Count, issues[] }`
`imageAudit`	Object	`{ totalImages, imagesWithAlt, imagesWithoutAlt, altTexts[], issues[] }`
`authorInfo`	Object/null	`{ name, url, sameAs[], jobTitle, source }`
`schemaTypes`	Array	All schema.org types detected (e.g., `["Product", "BreadcrumbList"]`)
`articleMetadata`	Object/null	`{ datePublished, dateModified, headline, description, wordCount, publisher }`
`breadcrumbs`	Object/null	`{ items: [{position, name, url}], issues[] }`

Geo / Local SEO (when `extractGeoData` is enabled)

Field	Type	Description
`geoTags`	Object/null	Geo meta tags (`{ region, placename, position, icbm }`)
`localBusiness`	Object/null	LocalBusiness schema data with NAP, geo coordinates, opening hours
`nap`	Object/null	Name, Address, Phone from Organization/LocalBusiness
`mapReferences`	Object/null	`{ googleMapsEmbeds[], placeIds[], cids[] }`

SEO audit (when `computeSeoScore` is enabled)

Field	Type	Description
`seoAudit`	Object	`{ score: 0-100, issues: [{severity, code, message}] }`

Optional extractors

Field	Type	Description
`rdfa`	Array	RDFa structured data (when `extractRdfa` is enabled)
`hCards`	Array	hCard/vCard contact data (when `extractHCard` is enabled)

SEO audit checks

The audit starts at 100 and deducts points for each issue found:

Check	Severity	Points	What it catches
Missing `<title>`	Error	-10	Core ranking signal
Title > 60 chars	Warning	-5	SERP truncation
Missing meta description	Error	-10	CTR impact
Description > 160 chars	Warning	-5	SERP truncation
Missing or multiple H1	Error	-10	Content hierarchy
Missing canonical URL	Warning	-5	Duplicate content risk
Missing og:title	Warning	-5	Social share CTR
Missing og:description	Warning	-5	Social share CTR
Missing og:image	Warning	-5	Social CTR (40-60% impact)
No structured data	Warning	-5	Rich results eligibility
Missing favicon	Warning	-5	Brand trust signal
Missing viewport	Warning	-5	Mobile-first indexing
Missing hreflang x-default	Warning	-5	International SEO trust
Missing author (on articles)	Warning	-5	EEAT signal
Missing datePublished (on articles)	Warning	-5	Freshness signal
> 50% images without alt	Warning	-5	Accessibility + AI
No BreadcrumbList (deep pages)	Info	-1	Navigation hierarchy

Use cases

Technical SEO audit

Crawl your site and get an instant SEO health check across every page. Identify missing titles, broken heading hierarchies, absent structured data, and more — with a prioritized issues list.

E-commerce competitive analysis

Extract product schema (pricing, availability, reviews, return policies), breadcrumb structures, and rich snippet eligibility from competitor product pages.

Local business intelligence

Scrape LocalBusiness schema from directories, review sites, or business websites. Extract NAP data, opening hours, geo coordinates, Google Place IDs, and CID numbers for lead generation or data enrichment.

International SEO validation

Audit hreflang implementations across multilingual sites. Catch the errors that 75% of implementations contain: missing x-default fallbacks, invalid ISO codes, missing self-references.

EEAT & content analysis

Extract author information, sameAs links to verified profiles, publication dates, and publisher data from article pages. Monitor how well your content signals expertise and authority.

Verify how pages will appear when shared on Facebook (Open Graph) and Twitter/X (Twitter Cards). Check for missing images, truncated descriptions, and incomplete metadata.

Content aggregation

Build news aggregators or content feeds by extracting article metadata, publication dates, authors, and descriptions from multiple sources in a single crawl.

Example output

News article (CNN)

{
  "url": "https://edition.cnn.com/2025/04/18/politics/...",
  "title": "Supreme Court temporarily pauses deportations under Alien Enemies Act",
  "canonical": "https://www.cnn.com/2025/04/18/politics/...",
  "language": {
    "htmlLang": "en",
    "contentLanguage": null,
    "ogLocale": "en_US"
  },
  "hreflang": {
    "tags": [
      { "lang": "en-gb", "url": "https://edition.cnn.com/..." },
      { "lang": "en-us", "url": "https://www.cnn.com/..." },
      { "lang": "x-default", "url": "https://edition.cnn.com/..." }
    ],
    "hasXDefault": true,
    "issues": ["Missing self-referencing hreflang tag"]
  },
  "authorInfo": {
    "name": "Tierney Sneed, John Fritze",
    "url": null,
    "sameAs": [],
    "jobTitle": null,
    "source": "meta"
  },
  "schemaTypes": ["NewsArticle", "Person", "ImageObject", "Organization", "WebPage", "NewsMediaOrganization"],
  "seoAudit": {
    "score": 79,
    "issues": [
      { "severity": "warning", "code": "TITLE_TOO_LONG", "message": "Title is 94 chars (recommended: max 60)" },
      { "severity": "warning", "code": "DESCRIPTION_TOO_LONG", "message": "Meta description is 267 chars (recommended: max 160)" },
      { "severity": "warning", "code": "MISSING_DATE_PUBLISHED", "message": "Article page missing datePublished (impacts freshness signals)" },
      { "severity": "info", "code": "NO_BREADCRUMBS", "message": "Deep page with no BreadcrumbList schema (helps navigation hierarchy)" }
    ]
  }
}

E-commerce product (Farfetch)

{
  "url": "https://www.farfetch.com/shopping/women/jacquemus-les-doubles-sandals-item-28543291.aspx",
  "title": "Jacquemus Les Doubles Sandals | Brown | FARFETCH",
  "canonical": "https://www.farfetch.com/shopping/women/jacquemus-les-doubles-sandals-item-28543291.aspx",
  "schemaTypes": ["ProductGroup", "ImageObject", "Brand", "Product", "Offer", "MerchantReturnPolicy", "UnitPriceSpecification", "BreadcrumbList", "ListItem"],
  "breadcrumbs": {
    "items": [
      { "position": 1, "name": "Women Home", "url": "/shopping/women/items.aspx" },
      { "position": 2, "name": "Jacquemus", "url": "/shopping/women/jacquemus/items.aspx" },
      { "position": 3, "name": "Shoes", "url": "/shopping/women/jacquemus/shoes-1/items.aspx" },
      { "position": 4, "name": "Heeled Sandals", "url": "/shopping/women/jacquemus/heeled-sandals-1/items.aspx" }
    ],
    "issues": ["Relative URL in breadcrumb position 1: \"/shopping/women/items.aspx\""]
  },
  "headings": {
    "h1Count": 1,
    "issues": ["Skipped heading level: h2 to h4"]
  },
  "imageAudit": {
    "totalImages": 6,
    "imagesWithAlt": 5,
    "imagesWithoutAlt": 1,
    "issues": ["1 image(s) missing alt attribute"]
  },
  "robotsMeta": { "robots": "noindex" },
  "seoAudit": { "score": 100, "issues": [] }
}

Wikipedia (RDFa + hCard)

{
  "url": "https://en.wikipedia.org/wiki/San_Francisco",
  "title": "San Francisco - Wikipedia",
  "canonical": "https://en.wikipedia.org/wiki/San_Francisco",
  "schemaTypes": ["Article", "Organization", "ImageObject"],
  "authorInfo": {
    "name": "Contributors to Wikimedia projects",
    "source": "json-ld"
  },
  "articleMetadata": {
    "datePublished": "2001-11-13T04:30:40Z",
    "dateModified": "2026-03-28T17:14:57Z",
    "headline": "consolidated city and county in California, United States",
    "publisher": { "name": "Wikimedia Foundation, Inc." }
  },
  "rdfa": ["... 114 RDFa items extracted ..."],
  "hCards": [{ "name": "San Francisco", "... ": "..." }],
  "imageAudit": {
    "totalImages": 119,
    "imagesWithAlt": 43,
    "imagesWithoutAlt": 76,
    "issues": ["64% of images missing alt text (accessibility + AI understanding)"]
  },
  "seoAudit": {
    "score": 80,
    "issues": [
      { "severity": "error", "code": "MISSING_DESCRIPTION", "message": "Page has no meta description" },
      { "severity": "warning", "code": "MISSING_OG_DESCRIPTION", "message": "Missing og:description meta tag" },
      { "severity": "warning", "code": "IMAGES_MISSING_ALT", "message": "64% of images missing alt text" }
    ]
  }
}

Technical details

Engine: CheerioCrawler (server-side HTML parsing, no JavaScript execution)
Runtime: Node.js 22, Apify SDK 3.5.3, Crawlee 3.15.3
Performance: Lightweight and fast — no browser overhead
Sessions: Automatic session rotation with cookie persistence
Proxies: Full proxy support including Apify Proxy residential groups
Retries: Up to 3 retries per request with automatic session rotation on blocks
Link following: Optional crawling with configurable depth

Limitations

No JavaScript rendering — Pages that require JS to load content (e.g., SPAs, IMDB) will return minimal data. Use a browser-based scraper for these.
Anti-bot protection — Some sites (Yelp, BBC, Medium) may block requests even with residential proxies. Results depend on proxy quality.
RDFa complexity — The RDFa extractor handles the common schema.org vocabulary case. Exotic namespace prefixes may not be fully resolved.

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