Why doesn’t the structured data testing tool detect your policy errors?

What is the difference between technical validation and policy compliance?

The structured data testing tool analyzes the JSON-LD, Microdata, or RDFa structure to ensure the code adheres to the syntax of schema.org vocabulary. It identifies formatting errors, missing mandatory properties, and incorrectly declared types.

What the tool does not do: verify that the marked content actually corresponds to what is visible on the page, that there is no misleading or manipulative markup, and that the declared entities comply with quality guidelines. An non-existent product, a fabricated review, or an off-topic FAQ can pass the tool without any issues as long as the JSON is correct.

Why does Google separate these two levels of validation?

Technical validation can be fully automated using JSON or XML parsers. Policy issues require contextual semantic analysis that algorithms alone cannot reliably resolve without massive false positives.

Therefore, Google prefers to isolate the technical layer in the public tool and reserve qualitative evaluation for manual audits or post-indexation detection systems. This also avoids publicly documenting all filtering criteria that spammers might exploit.

What types of policy violations go unnoticed by the tool?

The most common cases include invisible content markup for the user (hidden text marked in FAQ with display:none), inflated review ratings with no match to real customer reviews, and misleading product data (price, availability).

There is also markup for fictitious events to capture rich snippets, recipe markup on pages that do not contain a complete recipe, or JobPosting for expired or non-existent offers. The tool will technically validate all of this as long as the required properties are present.

• Technical validation: JSON-LD syntax, schema.org types, mandatory properties
• Policy validation: truthfulness of content, page/markup correspondence, adherence to quality guidelines
• Consequence: a site can have 100% valid markup and zero rich snippets if policies are violated
• Detection: policy violations are identified by post-crawl algorithms or manual audits, not by the testing tool

Is this technical/policy separation consistent with what we observe in the field?

Absolutely. Cases of sites with technically flawless markup but no rich snippets are very common. Search Console often shows "Valid" for structured data while displaying no rich results in the SERPs.

The reverse also exists: sites with a few minor technical warnings that still obtain review stars or FAQ boxes because the content adheres to policies. Google always prioritizes editorial compliance over syntactical perfection.

What nuances should be added to this statement?

Google does not specify where technical validation ends and policy begins. For instance, the tool sometimes detects “suspicious values” for certain properties (anomalous dates, malformed URLs), which pertains to superficial semantic validation.

Additionally, some types of markup (like speakable) require a manual whitelist from Google: the tool will never tell you if you are eligible or not. [To verify]: the exact boundary between what the tool checks and what falls under ranking systems remains vague and likely fluid.

What are the risks of relying solely on the testing tool?

The main danger is deploying technically valid but spammy markup, which can trigger manual actions or algorithmic filters. Google can ignore all the markup on the site or even penalize the domain if the manipulation is obvious.

Another risk: missing out on opportunities. Markup validated by the tool but poorly aligned with visible content will not generate rich snippets, thus resulting in zero ROI. Teams might then believe that “structured data does not work” when it is actually the editorial strategy that is at fault.

How to audit policy compliance beyond technical validation?

Start by extracting all marked entities from a sample of strategic pages (best-selling products, pillar articles). Manually compare each JSON-LD property with what the user actually sees in the visible DOM.

Check that marked reviews correspond to real customer reviews published on the page, that prices are up-to-date, and that FAQs reflect genuine questions. Use Rich Results Test and URL Inspection in Search Console to cross-reference the data: if Google indexes the markup but does not display a rich result, it is likely a policy issue.

What compliance errors are the most penalizing?

Invisible content markup (hidden text) is the classic mistake: JSON-LD describing text in display:none or visibility:hidden. Google can ignore all the markup of the site if this practice is systematic.

Ratings/reviews markup without verifiable reviews on the page often lead to manual actions. The same applies to job offers or expired events: leaving active markup on expired content violates guidelines and dilutes Google's trust in your domain.

Should you systematically validate each markup change?

Yes for the technical part via the testing tool, but also integrate a manual quality checklist into your deployment workflow. Markup that passes the tool but lies about product availability or invents off-topic FAQs will be counterproductive.

Automate what you can: scripts can check that each URL marked as Product has a visible price in the HTML, that each Review has an author and a date. The rest requires a trained human eye to judge semantic relevance.

• Systematically compare JSON-LD and visible content in the browser for each entity type
• Verify date/price/availability correspondence between markup and actual offer
• Eliminate all markup on hidden content (display:none, tabs not visible by default)
• Audit reviews: each Review schema must point to a real customer review published on the page
• Control freshness: remove Event/JobPosting/Offer markup on expired content
• Test using both Rich Results Test AND URL Inspection to cross-reference technical validation and actual eligibility