Schema.org Product Markup Validator

This product structured data validator parses the Schema.org Product markup on any page — JSON-LD, Microdata, or RDFa — and tells you, in plain language, which fields are present, which are malformed, and which gaps will keep search engines and AI answer engines from quoting your listing. Paste a snippet or a full page source and get an instant, field-by-field readout built for retail and marketplace teams optimizing for AI search.

What it checks

The validator inspects your markup against the Schema.org Product vocabulary and the properties that search and generative engines actually consume. It reports on:

• Syntax and parse errors — malformed JSON-LD, unclosed Microdata attributes, or RDFa that fails to resolve into a valid Product node.
• Required and recommended properties — name, image, description, sku, brand, plus a nested offers object with price, priceCurrency, and availability.
• Identifier coverage — global identifiers such as gtin, gtin13, mpn, and productID, which let engines disambiguate a 12 mm hex bolt or a specific MRO pump from near-identical listings.
• Offer and price integrity — currency codes against ISO 4217, availability against valid ItemAvailability enums (for example InStock, OutOfStock, BackOrder), and price values that are numeric rather than formatted strings.
• Review and rating consistency — whether aggregateRating, ratingValue, and reviewCount are present and internally consistent.
• Type and enum validity — values like availability or itemCondition that point at the correct Schema.org enumeration URLs rather than free text.
• AI-search readiness signals — presence of structured attributes (dimensions, material, color, unit of measure) that AI answer engines lean on when deciding which products to cite.

How it works behind the validator

Schema.org is a shared vocabulary maintained by a collaboration of major search providers; its Product type defines the canonical properties an engine expects on a product entity. JSON-LD — a JSON-based linked-data format — is the recommended way to express it, though Microdata and RDFa remain valid. This product structured data validator extracts every Product node from your input, resolves its @type and @context, then walks each property to confirm it exists, carries the right data type, and resolves to a legal enumeration where one is required.

Two checks matter most for AI search. First, identifier resolution: engines use gtin/mpn/sku to tie your listing to a known product entity, so missing or invalid identifiers leave you indistinguishable from competitors selling the same CPG item. Second, offer validity: an availability enum pointing at free text, or a price wrapped in currency symbols, is silently dropped by most parsers.

Related resources

FAQ

What is a product structured data validator? +

It is a tool that parses the Schema.org markup describing a product — its name, price, identifiers, availability, and attributes — and confirms that the markup is syntactically valid and contains the properties search and AI engines expect. It surfaces missing required fields and malformed values before they cost you visibility.

What's the difference between this and Google's Rich Results Test? +

Rich Results Test focuses on eligibility for specific Google rich result types. This validator checks your markup against the broader Schema.org Product vocabulary and against the signals AI answer engines use, including identifier coverage and attribute depth. Many teams run both: one for rich-result eligibility, this one for general structural and AI-search readiness.

Do I need GTIN or MPN in my Product markup? +

They are strongly recommended rather than universally required. Global identifiers let engines tie your listing to a known product entity and disambiguate it from near-identical items — a particular industrial fastener or CPG SKU rather than a generic match. Without them, your listing is harder to verify and easier to overlook.

Will valid Schema.org markup guarantee my products appear in AI search? +

No. Valid markup removes a structural blocker, but visibility also depends on attribute completeness, accuracy, and agreement between your structured data and the visible page. Treat validation as the floor, then improve coverage and citability on top of it.

Is it safe to paste my product markup here? +

Yes. The validator runs entirely in your browser. Your markup is parsed locally and never sent to a server, so there is no upload, no storage, and no file-size limit.