SchemaPaid

Schema Validation

Validates your JSON-LD for syntax errors, missing required properties, and deprecated fields. Invalid schema can hurt AI discoverability.

Why It Matters for AI Visibility

Invalid schema markup can be worse than no markup at all. AI engines like ChatGPT and Google AI Overviews use structured data to classify and understand your content. A malformed schema -- missing required fields, absent type declarations, or deprecated properties -- can cause AI engines to misinterpret your page or ignore the markup entirely. Schema errors mean AI engines cannot reliably extract the information you intended to provide. An Article schema without a headline or author gives incomplete data that AI systems may discard rather than risk citing inaccurately. A FAQPage schema missing its mainEntity field is structurally broken and provides zero value. Warnings represent missed opportunities rather than failures. Each missing recommended field -- like image, dateModified, or publisher on an Article -- is a piece of context that could help AI engines understand your content more fully and cite it more effectively. Perplexity and Google AI Overviews both use schema data to enrich their source understanding, so every field you complete strengthens your page's machine-readable profile.

How We Score It

Every JSON-LD schema block on your page is validated individually against schema.org requirements. The score starts at 10, and deductions are applied for each issue found. Errors -- missing required fields or a missing @type declaration -- deduct 2 points each. Required fields vary by schema type: Article needs headline, author, and datePublished; FAQPage needs mainEntity; Organization and Person need name. Warnings -- missing recommended fields, missing @context, or deprecated properties -- deduct 1 point each. A page with two errors would score 6 (10 minus 4). A page with three warnings would score 7 (10 minus 3). If your page has no JSON-LD schemas at all, the score is 0 since there is nothing to validate. Each issue is reported with its severity, schema type, and the specific field involved.
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How to Improve

  • 1

    Fix all required field errors first

    Required fields cause 2-point deductions each -- they are the highest-priority fixes. Check that Article schemas have headline, author, and datePublished. Check FAQPage has mainEntity. Check Person and Organization have name. Each fix recovers 2 points from your score.

  • 2

    Add @context to every schema block

    A missing `@context: "https://schema.org"` triggers a warning and a 1-point deduction. This field tells parsers which vocabulary you are using. It should be present in every JSON-LD block on your page as standard practice.

  • 3

    Add recommended fields to boost completeness

    After fixing errors, add recommended fields: image, dateModified, and publisher for Articles; jobTitle, url, and sameAs for Person; url, logo, and sameAs for Organization. Each missing recommended field costs 1 point, and each addition enriches what AI engines can extract.

  • 4

    Remove deprecated properties

    The analyzer flags mainEntityOfPage and copyrightYear as deprecated. Remove them or replace with current equivalents. Deprecated properties trigger warnings and signal to AI engines that your markup may be outdated.

  • 5

    Validate with Google Rich Results Test before publishing

    Run your schemas through Google's validator at search.google.com/test/rich-results to catch syntax errors that would prevent AI engines from parsing your markup at all. Syntax-level errors are invisible to the content-level validation this factor performs.

Before & After

Before
{
  "@type": "Article",
  "headline": "My Post"
}
After
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "My Post",
  "author": {"@type": "Person", "name": "Jane Smith"},
  "datePublished": "2025-01-15",
  "image": "https://example.com/hero.jpg",
  "dateModified": "2025-02-01",
  "publisher": {"@type": "Organization", "name": "Acme"}
}

Code Examples

Fully validated Article schema

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Complete Guide to Schema Validation",
  "author": {
    "@type": "Person",
    "name": "Jane Smith",
    "jobTitle": "Technical SEO Lead",
    "url": "https://example.com/authors/jane",
    "sameAs": ["https://linkedin.com/in/janesmith"]
  },
  "datePublished": "2025-01-15",
  "dateModified": "2025-03-01",
  "image": "https://example.com/images/schema-guide.jpg",
  "publisher": {
    "@type": "Organization",
    "name": "Acme Software",
    "logo": {
      "@type": "ImageObject",
      "url": "https://example.com/logo.png"
    }
  }
}

Frequently Asked Questions

What is the difference between an error and a warning?

Errors are missing required fields (headline, author, name, etc.) and cost 2 points each. Warnings are missing recommended fields, missing @context, or deprecated properties, and cost 1 point each. Fix errors first for the biggest score improvement per fix.

Which schema types does the validator check?

Article, BlogPosting, NewsArticle, FAQPage, Person, Organization, LocalBusiness, BreadcrumbList, WebPage, Product, and HowTo. Each type has defined required and recommended field lists that the validator checks against.

Can a single schema error bring my score to 0?

Multiple errors can. A schema with five errors (5 times 2 = 10 point deduction) would score 0. In practice, most schemas have one to three issues. The priority is fixing required field errors first since each one recovers 2 points.

Related Factors

Schema

JSON-LD Presence

Schema

Article / BlogPosting Schema

Schema

FAQPage Schema

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