AI Schema Validator

If you’re looking for the Structured Data Testing Tool, it’s gone. In 2020 Google deprecated its long-running Structured Data Testing Tool and migrated the underlying validator to Schema.org, where it now lives as the Schema Markup Validator at validator.schema.org. That tool checks your markup against the schema.org vocabulary, but it knows nothing about Google or AI - it only tells you whether the syntax is valid.

For its own purposes, Google replaced the Structured Data Testing Tool with the Rich Results Test. That’s a narrower tool: it only reports whether your page qualifies for a specific Google rich result, and stays quiet about any structured data that isn’t tied to one. So a thousand people a month still search for the old tool and land on something that either validates syntax with no context, or checks a short list of rich-result types.

Aveena’s validator sits in that gap. It validates your JSON-LD, checks rich-result readiness, and then scores how well AI engines can read and use your structured data - with the specific fix for every issue, not just a pass or fail.

To validate schema markup, start with the syntax: your JSON-LD has to be valid JSON, wrapped in a <script type="application/ld+json"> tag, with a recognised @type and a matching @contextof schema.org. From there, a good schema markup validator checks that each type has its required properties, flags recommended ones you’re missing, and confirms your URLs are absolute and use HTTPS.

Paste a URL or your raw JSON-LD into the validator above and it does all of that in one pass, then adds the layer the older tools don’t: an AI-readiness score that tells you whether your markup actually helps an engine understand and cite your page, and exactly what to change if it doesn’t.

The errors that come up again and again, and what each one means:

• Invalid JSON - a stray comma or unquoted key breaks the whole block, so the engine skips it entirely. This is the most common and the most costly.
• Missing required properties - a type like Article or Product needs certain fields; without them the markup is ignored for rich results.
• Unrecognised or misspelled properties- a property that isn’t part of the type is quietly dropped, so a typo silently removes the data you meant to add.
• Wrong type or broken references - the wrong @type, or an @id that points nowhere, leaves the engine unable to connect the entities on your page.
• Markup that doesn’t match the page - structured data has to reflect content a visitor can actually see; describing things that aren’t on the page can get the markup disregarded.

A handful of types do most of the work for AI visibility. Organization defines your brand as an entity - the single most important signal for being recognised and cited, and the foundation of entity SEO. Article attributes your content with an author and dates. FAQPage gives engines clean, quotable question-and-answer pairs. BreadcrumbList and WebSite describe your structure, and Product or SoftwareApplication carry pricing and detail for commercial pages.

You don’t need all of them. You need the ones that match your content, marked up correctly and consistently, so AI engines can attach the right facts to the right entity. The validator flags which types you have, which you’re missing, and where each one falls short for AI search.