Schema.org — Docs

Schema.org is a shared vocabulary for describing the meaning of web content. It is used by Google, Bing, AI crawlers, and other data consumers to understand what your pages are about — not just their text, but their structure and semantics.

What Schema.org does

Without structured data, an AI agent reading your page must infer meaning from context. With Schema.org, you explicitly tell it:

"This page is about a product named X, priced at Y."
"This page is an article written by Z on date D."
"This page belongs to an organization with these contact details."

This removes ambiguity and increases the chance of accurate citations.

Core types for agent-ready sites

Organization

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Acme Corp",
  "url": "https://example.com",
  "description": "We make widgets for the automotive industry.",
  "contactPoint": {
    "@type": "ContactPoint",
    "email": "contact@example.com",
    "contactType": "customer service"
  }
}

Add to every page or your homepage. Helps agents know who runs the site.

WebSite

{
  "@context": "https://schema.org",
  "@type": "WebSite",
  "name": "Acme Corp",
  "url": "https://example.com",
  "description": "Widgets for the automotive industry."
}

Article / BlogPosting

{
  "@context": "https://schema.org",
  "@type": "BlogPosting",
  "headline": "How to choose the right widget",
  "datePublished": "2025-01-15",
  "dateModified": "2025-02-01",
  "author": {
    "@type": "Person",
    "name": "Jane Smith"
  },
  "description": "A practical guide to widget selection."
}

Product

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Pro Widget X200",
  "description": "Industrial-grade widget for high-torque applications.",
  "offers": {
    "@type": "Offer",
    "price": "149.00",
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock"
  }
}

Critical for e-commerce. AI agents retrieve product data in real-time; structured data makes it unambiguous.

FAQPage

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is GEO?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "GEO (Generative Engine Optimization) is the practice of optimizing web content for AI agents and generative AI systems."
      }
    }
  ]
}

FAQ markup is especially valuable for GEO — it provides ready-to-cite question-and-answer pairs.

BreadcrumbList

{
  "@context": "https://schema.org",
  "@type": "BreadcrumbList",
  "itemListElement": [
    { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://example.com" },
    { "@type": "ListItem", "position": 2, "name": "Docs", "item": "https://example.com/docs" },
    { "@type": "ListItem", "position": 3, "name": "Schema.org" }
  ]
}

Helps agents understand where a page fits in your site hierarchy.

Implementation methods

Schema.org can be implemented as:

JSON-LD (recommended) — A <script> tag in your HTML <head>. See the JSON-LD guide.
Microdata — Inline attributes on HTML elements. More complex, rarely used today.
RDFa — Similar to Microdata. Used in some CMS ecosystems.

JSON-LD is preferred because it keeps structured data separate from presentation, is easy to maintain, and is the format most AI systems prefer.

Validation

After implementing Schema.org:

Google Rich Results Test — Validates JSON-LD and shows which rich result types your page qualifies for.
Schema.org Validator — General Schema.org validation.
Bing Webmaster Tools — Bing's structured data validator.