Schema Validator for SEO, AEO, and LLMs
Validate JSON-LD and Microdata on any page. Find missing fields, date errors, duplicate schemas, and gaps that affect rich results, AI citations, and answer engine visibility.
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Scans for JSON-LD and Microdata, validates fields and dates, checks for duplicates, and scores overall health.
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JSON-LD + Microdata
Detects both structured data formats, including @graph arrays.
Deep validation
Checks required fields, date formats, URLs, duplicates, and nesting.
Health score
0–100 score with breakdown across 5 quality dimensions.
Copy-paste fixes
Get corrected JSON-LD code for every issue found.
27+ schema types with priority tiers and benchmark data
Why it matters
Schema markup affects more than rich results
Structured data shapes how search engines, answer engines, and AI systems interpret your content. Gaps in your schema can mean gaps in your visibility.
Surface what engines actually read
Schema can exist but still be incomplete or inconsistent. See exactly what is being communicated to crawlers and AI.
Catch issues before they cost visibility
Missing fields, invalid dates, conflicting schemas, or weak markup can reduce your eligibility for enhanced results.
Improve machine-readability across surfaces
Good schema helps your content make more sense to Google, Bing, ChatGPT, Perplexity, and other discovery systems.
How we compare
Vizup vs other schema validators
Most validators check syntax. Vizup scores health, generates fixes, detects conflicts, and shows you what AI systems see.
| Feature | Vizup | Google Rich Results | Schema.org Validator |
|---|---|---|---|
| JSON-LD validation | |||
| Microdata detection | |||
| @graph support (WordPress/Yoast) | |||
| Health score (0-100) | — | — | |
| Priority tiers (P0-P3) with benchmarks | — | — | |
| Pitfall warnings (FAQ restricted, etc.) | — | — | |
| CTR/traffic benchmark data | — | — | |
| Copy-paste fix code | — | — | |
| Duplicate/conflict detection | — | — | |
| Page-type schema combos (builder) | — | — | |
| @id auto-generation & linking | — | — | |
| Schema tree visualization | — | — | |
| Missing schema recommendations | — | — | |
| Rich result eligibility check | — | ||
| Search preview | — | ||
| Inline editing & re-validation | — | — | |
| AI/AEO visibility insights | — | — | |
| No login required |
Understanding structured data
How schema markup works
Every web page contains content that humans can read — headings, paragraphs, images, prices. But search engines and AI systems need more explicit signals to understand what that content means. Schema markup provides those signals using a standardized vocabulary defined by schema.org.
When you add schema markup to a page, you're telling machines: "This page contains a Product called X, priced at $Y, with Z reviews." Or: "This is an Article by a specific author, published on a specific date, about a specific topic." Without this explicit labeling, engines have to guess — and guessing reduces precision.
The practical outcomes of good schema are visible: rich results in Google (star ratings, FAQ sections, product prices, event dates), improved Knowledge Panel accuracy, better AI answer engine citations, and clearer content classification across all discovery surfaces. Pages with valid, complete schema consistently earn more visual real estate in search results than pages without.
Common issues this validator detects
These are the problems we check for on every page. Each one can reduce your eligibility for rich results, weaken AI citation signals, or cause your markup to be ignored entirely.
Missing @context
Without "https://schema.org" as the context, search engines cannot identify the markup as valid structured data. This is the most common reason schema is ignored entirely.
Incomplete required fields
Each schema type has fields that Google requires for rich result eligibility. A Product without a name or an Article without a headline will not qualify for enhanced search features.
Missing author attribution
Articles and blog posts without proper author markup lose credibility signals. Google uses author information for E-E-A-T evaluation and AI systems use it for citation attribution.
No datePublished
Content without publish dates appears undated in search results and is harder for AI systems to assess for freshness. This is especially important for news, articles, and blog posts.
Invalid date formats
Dates must be in ISO 8601 format (e.g. 2026-01-15). Formats like 'January 5th, 2026' or 'Jan 5 2026' are not recognized by schema validators and will cause the date to be ignored.
Broken nesting
Schema types that reference other types — like a Product with an Offer, or an Article with a Publisher — need correct nesting. Flat or incorrectly nested markup loses relational meaning.
Duplicate/conflicting schemas
Two Organization schemas with different names or multiple conflicting Product entries confuse search engines. Each entity should have one authoritative schema definition per page.
Missing image references
Many rich result types — Product, Article, Recipe, Event — require image URLs. Without them, Google may not show the enhanced result even if other fields are valid.
Beyond Google: schema for AI and answer engines
Schema markup was originally designed for search engines, but its importance has expanded significantly with the rise of AI answer engines. Systems like ChatGPT, Perplexity, Gemini, and Claude don't just crawl web pages — they interpret them. And structured data gives these systems a much clearer signal about what a page contains, who created it, and how trustworthy the information is.
When an AI system encounters a page with proper Article schema including author, publisher, datePublished, and a well-structured description, it has far more confidence in citing that content than a page with no structured data at all. Author attribution chains (Person linked to Organization) are particularly valuable because they help AI systems establish source credibility — a critical factor in whether your content gets cited or passed over.
This is why schema validation is no longer just an SEO task. It's a visibility task that spans search, answer engines, knowledge graphs, voice assistants, and any system that needs to programmatically understand what your content means. The pages that win in this environment are the ones that make their meaning explicit — and schema is the primary mechanism for doing that.
Coverage
27+ schema types across 6 categories
We validate JSON-LD and Microdata for content, commerce, navigation, identity, local, and software pages — covering the types that matter most for SEO, AEO, and LLM discoverability.
Identity & Brand
How your organization, site, and people are represented
Content & Publishing
Articles, guides, FAQs, Q&A, how-tos, and video content
Commerce & Offers
Products, services, pricing, and customer reviews
Collections & Navigation
Category pages, listing pages, breadcrumbs, and search
Local, Events & Jobs
Physical locations, events, job postings, and place info
Software, Media & Recipes
Applications, SaaS products, media, and food content
Common questions
Frequently asked questions
Answers to the most common questions about schema markup, structured data validation, and how it affects search visibility and AI discoverability.
What is schema markup and why does it matter?
Schema markup is structured data you add to your website's HTML that helps search engines understand your content more precisely. Instead of just reading text, engines can identify that a page contains a product with a price, an article by a specific author, or a business at a particular address. This understanding enables rich results in search (star ratings, FAQ dropdowns, product cards) and helps AI systems like ChatGPT and Perplexity cite your content more accurately.
What is JSON-LD and how is it different from other schema formats?
JSON-LD (JavaScript Object Notation for Linked Data) is the format Google recommends for structured data. Unlike Microdata or RDFa, which are embedded inline within your HTML tags, JSON-LD sits in a separate <script> block — usually in the <head> section. This makes it easier to add, maintain, and debug without touching your page's visible markup. All major search engines and AI crawlers support JSON-LD.
How do I check if my website has schema markup?
Enter your page URL in the validator above. We scan the page for JSON-LD structured data and Microdata, extract each schema type found, validate required and recommended fields, check date formats, detect duplicates, and score your overall schema health. You can also check manually by viewing your page source and searching for 'application/ld+json' script blocks.
What happens if my schema markup has errors?
Errors in schema markup can range from minor to significant. Missing required fields mean your page won't qualify for rich results in Google. A missing @context means the entire block is ignored. Invalid date formats cause date information to be dropped. Conflicting schemas (like two different Organization names) confuse engines. Even valid-but-incomplete schema means you're leaving visibility on the table — both in search results and in AI answer engines that use structured data for citation decisions.
Which schema types are most important for SEO?
We categorize schema types into priority tiers based on real impact data. P0 (Essential): Organization and WebSite on homepages, Article on blog posts, Product on e-commerce pages, Recipe on food content, JobPosting on career pages, and LocalBusiness for physical locations. P1 (Recommended): BreadcrumbList on inner pages, Event, VideoObject, and HowTo. P2 (Nice to have): AggregateRating, QAPage, CollectionPage, and ItemList. Note that FAQPage rich results have been restricted since August 2023 to authority sites only.
Does schema markup directly affect Google rankings?
Schema markup is not a direct ranking factor, but the impact on visibility is significant and well-documented. Google's own case studies show Rakuten saw 1.5× more engagement time and Rotten Tomatoes achieved 25% higher CTR on pages with structured data. Jobrapido reported 4.5× more organic traffic after adding JobPosting schema. Pages with rich results take up more visual space in search results and consistently earn higher click-through rates. For AI answer engines, schema directly influences citation decisions.
What is the difference between required and recommended schema fields?
Required fields are the minimum Google needs to consider your page for a specific rich result type. Without them, the rich result simply won't appear. Recommended fields aren't mandatory but improve the quality and completeness of your enhanced listing. For example, a Product schema requires name and image, but adding price, availability, and reviews makes the rich result significantly more useful and likely to be shown. Our validator flags both types so you can prioritize effectively.
How does schema markup help AI answer engines like ChatGPT and Perplexity?
AI answer engines crawl and index web content to generate responses. When they encounter well-structured schema markup, they can more reliably identify what a page is about, who authored it, when it was published, and what claims it makes. This makes your content more likely to be cited as a source in AI-generated answers. Proper author attribution, datePublished, and clear Organization markup are particularly important for AI citation credibility.
How often should I validate my schema markup?
Validate after any significant page change — new content, redesigns, CMS updates, or template changes. Schema can break silently when pages are updated, especially on sites using dynamic content or JavaScript rendering. For important pages (homepage, key product pages, high-traffic articles), consider checking quarterly even without changes. If you're running a larger site, automated monitoring through a platform like Vizup can catch issues as they happen.
Can I have multiple schema types on one page?
Yes, and it's common practice. A typical homepage might include Organization, WebSite, and SearchAction schema. A blog post might have Article, BreadcrumbList, and FAQPage. An e-commerce product page might combine Product, BreadcrumbList, and AggregateRating. Each type goes in its own JSON-LD block or as an array within a single block. Google reads all of them independently. The key is that each schema type should accurately describe content that actually exists on the page.
What is a @graph in JSON-LD?
A @graph is a way to bundle multiple schema objects into a single JSON-LD block. Instead of having separate <script> tags for Organization, WebSite, and WebPage, you can include all three inside one @graph array. This is the approach used by WordPress plugins like Yoast SEO and Rank Math. Our validator fully supports @graph structures — we automatically extract and validate each node individually.
Does this validator check Microdata too?
Yes. While JSON-LD is the recommended format, many older websites use Microdata (HTML attributes like itemscope, itemtype, and itemprop). Our validator detects both formats, extracts the structured data, and validates it the same way. If we find Microdata on your page, we'll display it alongside any JSON-LD schemas so you can see your complete structured data picture.
What are FAQ and HowTo schema restrictions in 2024-2026?
Google significantly reduced FAQ and HowTo rich results starting in August 2023. FAQ rich results are now only shown for well-known, authoritative government and health websites. HowTo rich results were removed from mobile search results but still appear on desktop. If you're not a government or health authority, investing heavily in FAQPage schema may not yield visible rich results. Consider alternative approaches like QAPage schema for community Q&A, or focus on other high-impact types like Article, Product, or Recipe.
What do the priority tiers (P0-P3) mean?
We assign priority tiers to schema types based on their real-world SEO impact, backed by Google case studies and industry benchmarks. P0 (Essential) types like Article, Product, and Organization have direct, measurable impact on rich results and visibility. P1 (Recommended) types like BreadcrumbList and VideoObject provide meaningful but secondary benefits. P2 (Nice to have) types improve completeness. P3 (Optional) types are situational. This helps you focus effort where it matters most rather than adding schema for schema's sake.
Why should I avoid putting Product schema on category pages?
A common mistake is adding Product schema to category or collection pages. Google explicitly warns against this — Product schema should only be on individual product pages where a specific product can be purchased. Category pages should use CollectionPage or ItemList instead. Using Product schema on listing pages can trigger manual actions or cause Google to ignore your Product markup entirely, losing you rich results on the pages where they actually matter.
Next step
Missing schema? Build it from scratch.
Pick a schema type, fill in your details, and get valid JSON-LD you can copy directly into your page.
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