Content-Type Schema Alignment
stableCategory: entity · Methodology v4.5
friendly4AI reads two signals off your homepage HTML and URL path, then checks whether they line up.
Signal Source
- Source
https://{domain} (homepage HTML + URL path)- Kind
- html_dom
Score Bands
| Verdict | Condition |
|---|---|
| Pass | a specific JSON-LD @type (HowTo, DefinedTerm, Product/SoftwareApplication, ComparisonTable/ItemList, Review) is present and the URL path agrees with it, or the URL is neutral |
| Partial | the URL path signals a specific content type (e.g. /how-to/, /glossary/, /compare/, /review/, /product/) but no matching @type schema is present (schema signal is generic or absent) |
| Fail | the JSON-LD @type and the URL path both fire and point to conflicting content types (schema says one thing, URL says another) |
Description
Content-Type Schema Alignment checks whether your page's schema.org JSON-LD @type matches the content type its URL implies — say, a /how-to/ URL backed by HowTo schema. friendly4AI scores this for Bing, not Google. If the two signals agree, the page passes. If the URL implies a type but no matching schema exists, it scores partial. If schema and URL contradict each other, it fails.
What does this parameter measure?
friendly4AI reads two signals off your homepage HTML and URL path, then checks whether they line up.
The schema signal comes from the JSON-LD @type tokens. HowTo means a tutorial, DefinedTerm means a glossary, Product/SoftwareApplication/Service means a product, ComparisonTable/ItemList means a comparison, and Review/AggregateRating means a review. Generic Article or BlogPosting types tell the scanner nothing specific, so they count as no schema signal.
The URL signal comes from path fragments: /how-to/, /glossary/, /compare/, /review/, /product/.
Why does it matter for AI-readiness?
AI systems read structured data to classify a page before they decide what to do with it. A how-to page with no HowTo schema, or a glossary entry with no DefinedTerm, throws away the classification cue that triggers content-type-specific citations. The model sees plain prose where it could have read a labelled tutorial or definition.
Schema that contradicts the URL is worse. The mixed signal can suppress citations outright. When the schema and the URL agree, the page's type is unambiguous to the engines that route AI answers.
How is it scored?
This Discovery parameter (v4.5 methodology) scores the schema signal against the URL signal:
- Pass (100) — a specific
@typeis present and the URL agrees with it, or the URL is neutral (no content-type fragment) so there's nothing to contradict. - Partial (50) — the URL signals a specific type but no matching
@typeschema is present, for example a/glossary/URL with noDefinedTermschema. - Fail (0) — both signals fire and point to different content types: schema says one thing, URL says another.
When neither signal fires, the parameter is skipped (not_applicable_no_signal) instead of scored, so generic pages are never penalised.
How do I fix it?
- Add the schema.org
@typethat matches your content:HowTofor tutorials,DefinedTermfor glossary entries,SoftwareApplicationorProductfor product pages,Reviewfor reviews. - To close a partial, pair the content-typed URL with the schema it expects — a
/how-to/page should carryHowToJSON-LD. See structured-data-coverage and faq-schema for related schema checks. - To fix a conflict, align the schema with what the page actually is. Don't leave
Productschema on a/review/URL. - Use specific types over generic
Article/BlogPosting, which the scanner treats as no schema signal. - Keep your URL path conventions consistent with the page's real content type, so the URL signal stays trustworthy — see url-stability.
- Re-scan after the change to confirm the schema and URL signals now agree.
Version History
- Introduced
- v4.4
- Last changed
- v4.5
Key takeaways
- Signal: https://{domain} (homepage HTML + URL path)
- Category: Entity & Schema
- Passes when: a specific JSON-LD @type (HowTo, DefinedTerm, Product/SoftwareApplication, Co…