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Schema Markup Validator & Inspector

Inspect and validate structured data on any page. Detect JSON-LD errors, inspect extracted properties, see rich result eligibility signals, and get plain-English fixes — free, instant, no account needed.

Enter any public URL — we fetch the page and inspect all schema markup server-side.

✓ Check by URL or paste JSON-LD✓ Property-level inspection & tabbed view✓ Rich result eligibility signals✓ Severity-grouped errors with fixes✓ 15+ schema types validated
Background

What is Schema Markup?

A structured vocabulary that tells machines exactly what your content is — and why that matters for search and AI.

Schema markup (also called structured data) is a standardised vocabulary for annotating web content using the Schema.org standard. When added to a page, it gives search engines and AI systems explicit, machine-readable labels for your content.

Instead of a search engine having to infer that a page is about a recipe, schema markup explicitly states: this is a Recipe, with these ingredients, this cook time, and this rating. That precision enables richer understanding — and potentially richer display features.

Schema is most often implemented as JSON-LD — a compact block of structured JSON embedded in your page's HTML. It can also be expressed as Microdata or RDFa, though JSON-LD is the format strongly preferred by Google and most modern validators.

Example JSON-LD

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "What is Schema Markup?",
  "author": {
    "@type": "Person",
    "name": "Jane Smith"
  },
  "datePublished": "2025-01-01",
  "publisher": {
    "@type": "Organization",
    "name": "GEOflux"
  }
}

Three formats for structured data

JSON-LD

Preferred by Google. Embedded in a <script> tag.

Preferred
Microdata

Attributes added directly to HTML elements.

RDFa

HTML attribute extensions for linked data.

SEO & AI Visibility

Why Structured Data Matters for SEO and AI Systems

🔍

Machine-Readable Content

Schema markup converts implicit content meaning into explicit structured signals. This helps both search engines and LLMs process, classify, and surface your content more accurately — without guessing from prose alone.

🤖

AI and LLM Interpretation

Large language models increasingly draw on structured page signals when retrieving and summarising content. Well-formed schema helps AI systems assign your content to the right entity type, improving citation quality and relevance.

📊

Rich Display Eligibility

Search engines use structured data to evaluate pages for enhanced display features — FAQ dropdowns, star ratings, breadcrumb trails, product panels, and more. Correct schema is required for eligibility, though not a guarantee.

An honest perspective on schema and rankings

Schema markup does not directly improve your Google rankings. What it does is make your content more interpretable to machines — which increasingly determines how your content is discovered, summarised, and cited across AI-powered surfaces. Think of it as reducing ambiguity for search engines and AI systems, not as a shortcut to rankings.

Key Distinction

Schema Validity vs Rich Result Eligibility

These are two different things — and mixing them up is one of the most common misunderstandings in structured data.

1

Syntax Validity

Your JSON-LD parses without errors. @context and @type are present. This is the minimum bar — invalid JSON is silently ignored by all search engines.

Checked by: JSON parser, @context check, @type check

2

Semantic Completeness

Required and recommended fields are populated with real values. An Article with a headline but no author or datePublished passes syntax checks but is semantically incomplete.

Checked by: required field rules, recommended field rules

3

Rich Result Eligibility

The specific combination of fields required for enhanced display features is present. For example: a Product with name + offers.price + aggregateRating.ratingValue. This is evaluated separately from validity.

Shown by: Potential Rich Result Signals panel

The practical implication

A page can have syntactically valid schema and still show zero rich results in search. Conversely, satisfying all three layers above does not guarantee rich results — Google applies additional eligibility criteria including content quality and page signals. This tool shows you where you stand on each layer separately.

Common Issues

Common Schema Markup Mistakes

The most frequent implementation errors — and what to do about them.

✗Invalid JSON syntax

A missing comma, unclosed bracket, or stray character breaks the entire block. Parsers discard invalid JSON — your schema may look correct in code but be completely invisible to search engines.

Fix: Use a JSON validator before pushing schema changes. This tool flags syntax errors automatically.

✗Missing @context or @type

Every JSON-LD block needs "@context": "https://schema.org" and a valid "@type". Without these, parsers cannot determine what vocabulary you are using or what entity type the block describes.

Fix: Always include both @context and @type as the first properties of every block.

⚠Missing required fields

Each schema type has fields that are required for parsing engines to use the markup. For example, an Article without a headline and author is considered incomplete.

Fix: Refer to Schema.org documentation for each type, or use this validator to surface missing required fields.

⚠Duplicate schema blocks

Multiple blocks of the same type on one page can confuse parsers — especially if they contain conflicting data. This is a common CMS / plugin issue.

Fix: Audit your page for duplicate JSON-LD blocks and consolidate them into a single, complete block per type.

⚠Using microdata in modern CMS

Older themes and plugins often inject Microdata attributes into HTML. While still parsed, Microdata is harder to maintain and more prone to conflicts with layout changes.

Fix: Migrate structured data to JSON-LD for easier management and more reliable parsing.

⚠Schema that misrepresents content

Adding schema for content types that do not match your page — for example, adding FAQPage schema to a page with no actual FAQ — can result in manual actions from search engines.

Fix: Only use schema types that accurately describe the primary content on the page.

About This Tool

How This Schema Validator Works

What we check — and what pass, warning, and fail mean.

🌐

Page fetch

We fetch the full HTML of the provided URL server-side, with no browser-based JS rendering limitations.

🔎

JSON-LD extraction

We extract all <script type="application/ld+json"> blocks from the page HTML and attempt to parse each one.

📋

Microdata detection

We scan for itemscope attributes to detect and count Microdata usage on the page.

🔗

RDFa detection

We detect RDFa-style structured data through typeof and property attributes in the markup.

⚙️

JSON syntax validation

Each JSON-LD block is parsed as strict JSON. Syntax errors are flagged with the specific parse error message.

📐

Required field check

For each detected schema type, we check whether all required fields (as per Schema.org) are present.

✨

Recommended field check

We flag missing recommended fields that improve schema richness for machine understanding and display eligibility.

🏷️

@type detection

We identify all schema types present on the page, including nested types and @graph arrays.

♻️

Duplicate detection

We flag pages that have multiple JSON-LD blocks for the same schema type, which may create conflicting signals.

Understanding Results

✓ Pass

This check meets best practice. No action needed.

⚠ Warning

Recommended improvement. Schema will still be parsed, but this is worth addressing.

✗ Fail

Critical issue. Search engines may be unable to parse or use this schema.

FAQ

Frequently Asked Questions About Schema Markup

What is schema markup?+
Schema markup (also called structured data) is a standardised method of annotating web page content so that search engines, AI systems, and other machines can understand it more precisely. It uses a shared vocabulary from Schema.org and is most commonly implemented as JSON-LD — a small JavaScript block embedded in your page's HTML.
What is a schema markup validator?+
A schema markup validator checks whether your website's structured data is correctly formatted and contains the right fields for the schema types you're using. It can detect JSON syntax errors, missing required or recommended properties, unrecognised types, and other implementation issues that could prevent search engines from interpreting your schema.
How do I validate schema markup?+
To validate schema markup, enter a URL into a schema checker tool (like this one). The tool fetches the page, extracts any JSON-LD, Microdata, or RDFa structured data blocks, parses them, and cross-references each block against Schema.org's field requirements. Alternatively, you can paste raw JSON-LD directly to validate it without fetching a live URL.
What is the difference between schema validity and rich result eligibility?+
Schema validity and rich result eligibility are two separate things. A schema block is considered valid if its JSON is syntactically correct, it has a proper @context and @type, and it includes the required fields for that type. Rich result eligibility is a higher bar — it means the schema contains enough of the specific signals (like ratingValue, ratingCount, or itemListElement entries) that Google may choose to enhance your SERP listing. A valid schema can still be ineligible for rich results if key recommended fields are missing.
Which schema types can trigger rich results in Google?+
Google supports rich results for a specific set of schema types. These include FAQPage (FAQ dropdown expansions), Product (price and rating display), BreadcrumbList (breadcrumb trails), Recipe (recipe cards with image and ratings), Event (event details in search), JobPosting (job listings in Google Jobs), Article and NewsArticle (article signals for Top Stories), VideoObject (video rich results), and LocalBusiness (enhanced local panels). Having valid schema for these types makes your page eligible for consideration — but does not guarantee appearance.
What does it mean if my schema has warnings but no errors?+
Warnings indicate that your schema is syntactically valid and parseable, but is missing recommended fields that could improve its quality and usefulness. For example, a Product schema without an aggregateRating or image is technically valid — but those missing fields reduce its chances of qualifying for rich result enhancements. Addressing warnings is good practice, especially for commercial or high-priority pages.
What is JSON-LD?+
JSON-LD (JavaScript Object Notation for Linked Data) is the recommended format for implementing structured data on web pages. It is a small block of JSON code placed within a script tag in your page's HTML. JSON-LD is preferred over Microdata and RDFa because it is easier to maintain and less likely to interfere with your HTML structure.
Does schema markup guarantee rich results in Google?+
No. Schema markup is a necessary but not sufficient condition for rich results. Google requires correct structured data before it will consider a page for enhanced display features like star ratings, FAQ dropdowns, or breadcrumb trails. However, having valid schema does not guarantee these features will appear. Other factors, including content quality, page authority, and how Google's algorithms assess the page, all play a role.
Does schema markup help AI systems understand my content?+
Yes, schema markup helps both AI-powered search systems and large language models interpret your page's content more accurately. Structured data provides explicit context — for example, clearly labelling your content as a Product with a brand, price, and rating — that AI systems can use to classify, summarise, and cite your content more reliably. It is a practical way to make your content more machine-readable.
What schema types should I use?+
The right schema type depends on your page's content. Use Article or BlogPosting for editorial content, Product for ecommerce pages, FAQPage for FAQ sections, LocalBusiness for location-based businesses, BreadcrumbList for navigation breadcrumbs, and Organization or WebSite for brand identity signals. Most pages should have at least an Organization or WebSite block alongside their page-specific schema.
What is microdata and RDFa?+
Microdata and RDFa are older formats for embedding structured data directly into HTML attributes. While they are still supported by some parsers, JSON-LD is now the preferred format because it separates structured data from your HTML layout, making it easier to read, update, and validate. If your site still uses Microdata or RDFa, consider migrating to JSON-LD.
What does "missing recommended fields" mean?+
Recommended fields are properties that Schema.org and search engines highlight as beneficial even if not strictly mandatory. For example, an Article schema should ideally include an image, dateModified, and publisher alongside the required headline, author, and datePublished. Missing recommended fields does not break your schema, but it does reduce the richness of information available to machines and may affect eligibility for enhanced display features.
Is schema markup the same as SEO meta tags?+
No. Schema markup and meta tags serve different purposes. Meta tags like title and meta description communicate information to browsers and basic search result appearances. Schema markup communicates structured semantic information about your content — what type of thing it is, who created it, what it costs, when it was published — to machines that can act on that context. Both are important components of a complete technical SEO strategy.

Check Another Page's Schema

Validate structured data on any URL — a competitor, a product page, or your own site after making improvements.

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