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On this page What it checks
  1. What it checks
  2. How the markup generator works
  3. Related resources
  4. FAQ

Schema.org Product Markup Generator

Free schema.org product markup generator that turns product attributes into valid Product JSON-LD in your browser. No login, no upload, instant output.

published ai-searchretail-marketplaces

This schema.org product markup generator turns your product attributes — title, brand, GTIN/MPN, price, availability, and ratings — into valid Product JSON-LD that search engines and AI answer engines can parse. Fill in one product’s fields — or a whole variant family — and copy structured markup you can drop straight into a page template.

Schema.org Product Markup Generator

Runs in your browser · no upload
Fill in what you have; empty fields are left out of the markup.

Product

Offer

Shipping details (optional)
Return policy (optional)
Ratings & review (only if they're shown on the page)
Variants (ProductGroup) (optional)

Turns the product into a ProductGroup with one nested Product per variant. The name, brand, description and images above become the group's; each variant inherits the offer settings and can override price and availability.

Varies by

Your JSON-LD

 
     
    Checks
     
     
      
     
     Generated from what you enter: the markup must match what the page actually shows, and valid markup doesn't guarantee rich results. See how Claro generates accurate product data for every SKU → 
     
     

    What it checks
    

    The generator builds a Product object and validates the inputs as you fill them in. Specifically, it:
    

      

    • Maps your fields to the correct Schema.org properties — name, brand, sku, gtin/gtin13/mpn, description, image, category, and additionalProperty for spec attributes.
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    • Builds a nested offers block with price, priceCurrency (ISO 4217), availability (an ItemAvailability URL such as InStock or OutOfStock), and optional priceValidUntil.
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    • Adds aggregateRating and review only when you enter ratings or a reviewer, and flags a rating without a rating count, so you never emit ratings markup with no underlying data.
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    • Flags missing recommended properties that AI search and rich results lean on — for example a product with a price but no availability, or no image URL.
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    • Validates identifier formats — checks that a GTIN is 8, 12, 13, or 14 digits with a valid GS1 check digit, emits it under the most specific property (gtin13, gtin12…), and warns when both gtin and mpn are absent, since AI agents use those to match a SKU to a canonical product.
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    • Handles shipping, returns, and variants — optional shippingDetails, hasMerchantReturnPolicy, and a ProductGroup with hasVariant products when you list variants, each checked against Google’s merchant listing requirements.
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    • Outputs ready-to-paste JSON-LD wrapped in a <script type="application/ld+json"> tag, plus a plain-language summary of what each block does.
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    How the markup generator works
    

    Schema.org Product is the shared vocabulary that Google, Bing, and large language model answer engines read to understand a product page. JSON-LD is the recommended serialization: a single JSON block in the page <head> or <body> that describes the product independently of your visible HTML. A vague title like “Heavy Duty Pump 2HP” is hard to disambiguate, but Product markup with a brand, an mpn, and structured additionalProperty entries (flow rate, voltage, port size) gives a machine an unambiguous record.
    

    The same pattern applies across categories. A CPG item carries a gtin13 and net weight, an industrial MRO part carries an mpn and material spec, a furniture SKU carries dimensions and finish, and each maps cleanly onto Product plus offers. The generator follows the published Schema.org property names and Google’s documented recommendations for product structured data — it does not invent properties or guess at values you have not entered.
    


    Generating markup for one product is straightforward. Generating it correctly across tens of thousands of SKUs — where missing GTINs, inconsistent units, and stale availability quietly break the markup — is a data problem, not a templating one. That is where a canonical product-data layer matters: Claro’s AI search and GEO capability keeps the underlying attributes complete, deduplicated, and validated so the markup your templates emit is accurate at scale.
    


      
    Glossary
     
     
    What Is Schema.org Product Structured Data?
      
     
    The vocabulary behind this tool and why AI answer engines depend on it.
          
    Tool
     
     
    Schema.org Product Markup Validator
      
     
    Already have markup? Check existing JSON-LD for errors and missing recommended fields.
          
    Playbook
     
     
    Generate Schema.org Product Markup at Scale
      
     
    Move from one-off generation to automated, template-driven markup across your full catalog.
          
    Guide
     
     
    Product Data Requirements for AI Search Visibility
      
     
    What attributes AI search engines need before they will cite your products.
          
    Tool
     
     
    Product Data Completeness Scorer
      
     
    Score how complete your attributes are before you generate markup from them.
          
    

    FAQ
    

      What is Schema.org Product markup?   
     
    Schema.org Product markup is structured data that describes a product — its name, brand, identifiers, price, and availability — using a shared vocabulary that search engines and AI answer engines understand. It is most commonly added as JSON-LD, a single script block on the product page that lets machines read the product as a record rather than guessing from page text.
     
     
    

      Do I need GTIN or MPN in my product markup?   
     
    They are strongly recommended whenever a global identifier exists. A GTIN (the number encoded in a UPC or EAN barcode) or manufacturer part number lets engines match your listing to a canonical product across the web, which improves both rich result eligibility and how reliably AI agents can cite your specific SKU. For unbranded or custom items without a GTIN, supply brand plus mpn instead.
     
     
    

      Is JSON-LD or Microdata better for product markup?   
     
    JSON-LD is the format Google explicitly recommends and the one this generator outputs. It lives in a self-contained script block, so it does not entangle structured data with your visible HTML and is far easier to template and maintain across a large catalog. Microdata and RDFa are still valid Schema.org syntaxes but are harder to keep correct at scale.
     
     
    

      Why does my generated markup show a warning about availability?   
     
    The offers block should include an availability value — a Schema.org ItemAvailability URL such as InStock, OutOfStock, or PreOrder. If you enter a price but no availability, AI search and rich results may treat the offer as incomplete or stale. The generator flags this so you can supply current stock status before publishing.
     
     
    

      Does adding Product markup guarantee rich results or AI citations?   
     
    No. Valid markup makes a product eligible for rich results and easier for AI engines to parse and cite, but eligibility is not a guarantee — engines apply their own ranking and quality criteria. Accurate, complete underlying data and a clean feed matter just as much as the markup syntax itself.
     
     
     
     
     
    Claro
     
     See where your catalog breaks — free 
     
    Claro runs this automatically: resolve identity, fill missing attributes, validate updates, and write clean records back into your PIM/ERP. Upload a sample supplier file for a free catalog audit.
      Get a free catalog audit