Schema markup validator
Check your website's JSON-LD structured data and see how well AI engines can understand your content. Get specific recommendations to improve your schema markup for better AI visibility.
Why structured data matters for AI visibility
Structured data is the language your website uses to communicate with machines. While Google uses structured data for rich results (star ratings, FAQ snippets, product cards), AI engines use it for something more fundamental: understanding what your content is about, who created it, and how reliable it is.
When ChatGPT, Perplexity, or Claude crawls your website, properly implemented JSON-LD structured data helps them extract accurate information about your organization, products, articles, and expertise. Without it, AI engines must infer this information from unstructured HTML — a process that's slower, less accurate, and more prone to misinterpretation.
When to use this tool
The schema markup validator is essential at several points:
- Website audit — Before starting any generative engine optimization work, validate that your existing structured data is correct and comprehensive
- After website changes — CMS updates, theme changes, or redesigns can break structured data. Validate after any significant website modification
- New page types — When adding product pages, FAQ sections, author profiles, or other structured content, validate that the schema is properly implemented
- Competitive analysis — Check competitor pages to understand what structured data they implement that you might be missing
What the results tell you
The validator provides a comprehensive structured data assessment:
Schema type inventory. See every JSON-LD schema type present on your page — Organization, Article, Product, FAQPage, BreadcrumbList, and others. Missing schema types are highlighted as opportunities.
Validation results. Each schema is checked against Schema.org specifications for required properties, correct data types, and proper nesting. Errors that would confuse AI crawlers are flagged with specific fix instructions.
AI-specific recommendations. Beyond Schema.org validation, the tool recommends schemas that specifically help AI engines: Organization schema for brand identity, Article schema with author information for content credibility, and FAQ schema for question-answering AI features.
Completeness scoring. Each schema type receives a completeness score. A Product schema with only name and price scores lower than one that includes description, reviews, availability, and brand — because AI engines extract more useful information from complete schemas.
Best practices for AI-friendly schema
For maximum AI brand visibility, implement these schema types as a baseline:
- Organization — On your homepage, with name, logo, social profiles, and contact information
- Article or BlogPosting — On content pages, with author, date, and publisher information
- Product — On product pages, with complete pricing, availability, and review data
- FAQPage — On FAQ content, which AI engines use for question-answering features
- BreadcrumbList — Site-wide, helping AI engines understand your content hierarchy
Use the AI brand mention checker to verify that improved structured data translates into more accurate AI mentions. Check your visibility score before and after implementation to measure impact.
Features
- Validate JSON-LD structured data on any URL
- Check for Organization, Article, FAQ, Product, and other schemas
- AI-specific recommendations (what schemas help AI engines most)
- Completeness score for each schema type
- Common error detection and fix suggestions
- Side-by-side comparison with competitor pages
How it works
- 1Enter a URL to validate
- 2We fetch the page and extract all JSON-LD structured data
- 3Each schema is validated against Schema.org specifications
- 4You receive a report with scores, errors, and AI-specific recommendations
Frequently asked questions
Which schema types are most important for AI?
Organization (brand identity), Article (content), FAQPage (Q&A content), Product (e-commerce), and BreadcrumbList (site structure) are the most impactful for AI visibility.
Does structured data directly improve AI visibility?
While AI engines don't use structured data the same way Google does for rich results, comprehensive schema markup helps AI engines accurately understand your content, brand, and offerings — which improves how they reference you in responses.
What's the difference between this and Google's testing tool?
Google's tools focus on valid schema for rich results. Our tool evaluates schema from an AI visibility perspective — checking for completeness and recommending schemas that specifically help AI engines understand your content better.
How often should I validate my structured data?
Validate after any website update, CMS change, theme modification, or new page template deployment. At minimum, run a quarterly audit across your key page types (homepage, product pages, blog posts, FAQ pages) to catch any regressions. Structured data can break silently during updates, so regular checks prevent extended periods of degraded AI visibility.
Does my CMS automatically generate structured data?
Most modern CMS platforms (WordPress, Shopify, Squarespace) generate some structured data automatically, but it's often incomplete for AI visibility purposes. Default schema typically covers basic Organization and Article types but may miss Product details, FAQ markup, or comprehensive author information. Use this validator to check what your CMS generates and identify gaps to fill with manual implementation or plugins.
Can incorrect structured data hurt my AI visibility?
Yes. Incorrect structured data is worse than no structured data. If your Product schema lists the wrong price, or your Organization schema has an outdated name, AI engines may use that incorrect data when generating responses about your brand. Validation ensures accuracy before AI crawlers consume your structured data. Fix errors promptly when detected.
What is JSON-LD and why is it preferred over other structured data formats?
JSON-LD (JavaScript Object Notation for Linked Data) is a method of encoding structured data using JSON. It's preferred over alternatives like Microdata and RDFa because it's placed in a script tag separate from your HTML, making it easier to implement and maintain without modifying page markup. Google recommends JSON-LD, and AI crawlers parse it most reliably. All schema recommendations from this tool use the JSON-LD format.
Should I add structured data to every page on my site?
Focus on your most important page types first: homepage (Organization), product pages (Product), content pages (Article), and FAQ pages (FAQPage). BreadcrumbList schema should be site-wide. You don't need structured data on every page — prioritize pages that you want AI engines to understand accurately and reference in their responses. Utility pages, privacy policies, and terms of service generally don't need structured data for AI visibility.
Validate your structured data
Check your schema markup and get AI-specific recommendations to improve visibility.
