What does the schema validity check look for?
It checks that your structured data is not just present but correct and useful. Specifically:
- Valid syntax — the JSON-LD parses without errors and is well-formed.
- Appropriate type — it uses a schema.org @type that genuinely matches the content (Article for an article, Product for a product).
- Completeness — it includes the properties that type expects, rather than being generic or missing required fields.
Valid markup with an appropriate type passes; valid but generic or incomplete schema is a warning; a syntax error or the wrong type is a fail.
How is it evaluated, and how is it scored?
GEObubbly parses your structured data and checks its syntax, type and completeness. It's a core, scored Structured Data check that runs directly against the page's markup.
Why schema validity matters for SEO and GEO
Adding structured data only pays off if it's valid. Three things can go wrong. First, syntax errors: a malformed JSON-LD block — a missing comma or bracket — may be ignored entirely, so all your markup effort is wasted. Second, the wrong type: marking a blog post as a Product, or using an overly generic type, misrepresents the content and can be treated as misleading. Third, incompleteness: each schema type has properties engines expect (an Article needs a headline and author; a Product needs offers to show price), and omitting them means you won't qualify for the corresponding rich result. Invalid or mismatched schema isn't just unhelpful — it can actively confuse engines about what your page is. The fix is to validate your markup (Google's Rich Results Test and the schema.org validator both do this), use the correct type for the content, and fill in the required properties. For GEO, accurate schema is what lets AI engines trust your machine-readable description, so validity directly affects how reliably you're understood.