Free Tool

Validate Your Schema Markup for AI

Check if your JSON-LD structured data contains the properties AI models actually use for citations, attribution, and answer generation.

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AI Readiness Score

What This Tool Checks

Most schema validators check if your markup is syntactically valid and eligible for Google Rich Results. That's useful for SEO, but it tells you nothing about how AI language models interpret your structured data.

This tool evaluates AI readiness — whether your schema contains the specific properties that models like ChatGPT, Claude, and Perplexity use when deciding to cite your content. These include author attribution chains, freshness signals like dateModified, content descriptions, and answer completeness indicators.

A page can pass Google's Rich Results Test perfectly and still be invisible to AI if it's missing the properties AI models parse for.

Schema Types We Analyze

Article

The most common schema for blog posts, news, and long-form content. AI models extract headline, author attribution (name + type), publication and modification dates, description, and content categorization. Missing author information is one of the biggest reasons AI skips citing articles — it can't verify the source.

FAQPage

AI models parse FAQ schemas to directly answer questions. Each Question needs an acceptedAnswer with substantive text (50+ characters). Short or missing answers mean AI can't use the FAQ as a reliable source. The number of questions also matters — pages with 3+ questions are more likely to be surfaced.

HowTo

Step-by-step instructions are highly valued by AI for procedural queries. Each step needs descriptive text, and the schema benefits from totalTime and estimatedCost for completeness. AI uses this to generate step-by-step answers with proper attribution.

Product

For e-commerce, AI extracts pricing, availability, ratings, and reviews to answer product comparison queries. Missing offers or aggregateRating data means your product is less likely to appear in AI-generated shopping recommendations.

Organization

Organization schema helps AI establish entity identity and trust. Logo, contact information, social profiles (sameAs), and a clear description help AI confidently attribute information to your brand across different contexts.

Why AI Cares About Your Schema

Search engines use structured data primarily for rich snippets — visual enhancements in search results. AI models use it for something fundamentally different: establishing trust and enabling citation.

When a language model encounters your content, it needs to decide: Can I trust this source? Can I attribute it? Is it current? Structured data answers all three questions explicitly, instead of forcing the model to infer answers from unstructured HTML.

Pages with complete, well-structured schema markup are significantly more likely to be cited in AI-generated responses. This is especially true for YMYL (Your Money, Your Life) topics where attribution and authority matter most.

For a deeper dive into how structured data affects AI visibility, read our guide: How Structured Data Impacts AI Visibility.

Frequently Asked Questions

How is this different from Google's Rich Results Test?

Google's Rich Results Test checks whether your schema qualifies for search result enhancements like star ratings or FAQ dropdowns. This tool checks whether your schema contains the properties that AI language models actually parse when generating answers — things like author attribution chains, freshness signals (dateModified), answer completeness, and content description quality. A page can pass Google's test perfectly and still be invisible to AI.

What schema types does this tool validate?

The tool has specialized AI-readiness rules for Article, FAQPage, HowTo, Product, and Organization schemas. These are the types AI models most commonly extract data from. For any other schema type (LocalBusiness, Event, Recipe, etc.), it runs cross-type checks including valid JSON structure, @context, @type presence, and empty array detection.

What does the AI readiness score mean?

The score rates how well your structured data serves AI models, not search engines. Each schema starts at 100 points. Critical missing properties (like headline for Article or mainEntity for FAQPage) deduct 20 points. Important AI-specific properties (like author attribution, dateModified, description) deduct 8 points. Nice-to-have properties deduct 3 points. The overall score averages all schemas plus a schema diversity bonus (+3 points per distinct type, max +10), capped at 100.

Does this tool work with WordPress and @graph schemas?

Yes. WordPress sites using Yoast, Rank Math, or similar SEO plugins typically output all their schemas inside a single @graph array. This tool automatically detects and flattens @graph arrays into individual schemas, then validates each one separately with the appropriate type-specific rules.

Why does AI care about my schema markup?

AI language models use structured data as a reliable signal for attribution and factual accuracy. When your schema includes a clear author, publication date, organization details, and content descriptions, AI can confidently cite your page as a source. Without these signals, AI has to infer context from the page content alone — which is less reliable and makes your content less likely to be referenced in AI-generated answers.

Can I paste HTML instead of entering a URL?

Yes. Toggle the input mode to "Paste HTML" and paste your page's full HTML source code or just the JSON-LD script blocks. This is useful for testing pages behind login walls, local development sites, or schema markup you're writing before deploying it.

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Ruslan S. Senior Software Engineer