TL;DR. Structured data is how Google + LLMs disambiguate your pages. A broken @type drops you from rich results; a subtle datePublished timezone error can stop citation for current-event queries.
The Speakable Gen. is the audit you reach for when you already suspect a problem in this dimension and need a fast, copy-paste-able fix list. It reuses the same chrome as every other jwatte.com tool — deep-links from the mega analyzers, AI-prompt export, CSV/PDF/HTML download — but the checks it runs are narrow and specific to the dimension described above.
Paste a page URL and get a ready-to-paste SpeakableSpecification JSON-LD block that marks the paragraphs AI assistants should read aloud — tuned for Google Assistant, voice search, and AI citations.
Why this dimension matters
Structured data is how Google, Bing, and LLM crawlers disambiguate your page. A missing @type or broken sameAs URL can drop a page out of Rich Results eligibility entirely; a subtle datePublished timezone error can cause AI engines to cite the wrong date and — over time — stop citing you for current-event queries.
Common failure patterns
sameAspointing to abandoned social profiles — the entity graph requires sameAs URLs to resolve and identify the same entity. Stale Twitter/X accounts, defunct Facebook pages, broken LinkedIn URLs all dilute the signal. Prefer Wikidata + ORCID + verified merchant profiles.- Nested schema that never serializes — Google's Rich Results Test accepts invalid nesting that is later rejected by the crawl-indexing stage. Always validate against both Rich Results Test AND the Schema Markup Validator; they catch different issues.
- Mismatch between visible content and schema — FAQPage schema with 8 Questions while the visible HTML only shows 3 is a Structured Data Spam Policy violation. The audit flags when visible-to-schema ratios diverge by more than ~20%.
- Missing
Organizationat the site root — the site-wide Organization schema is the anchor for Knowledge Panel eligibility. It needsname,url,logo,sameAs(to socials + Wikidata), and — for businesses —address+contactPoint.
How to fix it at the source
Centralize schema in your template layer (Nunjucks include, Next.js <Head>, etc.) so every page inherits the site-wide Organization and Breadcrumb; per-page types (Article, Product, FAQPage) extend from there. Run the Schema Markup Validator + Rich Results Test on every new page type before deploying; both catch different failure modes.
Thresholds that matter
| Signal | Target |
|---|---|
| JSON-LD blocks per page | 2+ is healthy (site-wide Org + page-specific type); 1 is acceptable; 0 is a miss. |
| FAQPage minimum Questions | 2 (Google), but 5–10 is the practical floor for rich-result eligibility. |
| sameAs depth for Organization | 3+ verified profiles — X/LinkedIn + Wikidata + a business listing is the starter set. |
| Article required fields | headline, image, datePublished, dateModified, author (with @id + url). |
Example fix
Site-wide Organization schema (Nunjucks include):
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Organization",
"@id": "https://yoursite.com/#organization",
"name": "Your Business",
"url": "https://yoursite.com",
"logo": {
"@type": "ImageObject",
"url": "https://yoursite.com/logo.png",
"width": 600,
"height": 60
},
"sameAs": [
"https://www.wikidata.org/wiki/Q123456",
"https://www.linkedin.com/company/your-business",
"https://x.com/yourbusiness"
],
"contactPoint": [{
"@type": "ContactPoint",
"telephone": "+1-208-555-0100",
"contactType": "customer service",
"areaServed": "US"
}]
}
</script>
When to run the audit
- After a major site change — redesign, CMS migration, DNS change, hosting platform swap.
- Quarterly as part of routine technical hygiene; the checks are cheap to run repeatedly.
- Before an investor / client review, a PCI scan, a SOC 2 audit, or an accessibility-compliance review.
- When a downstream metric drops (rankings, conversion, AI citations) and you need to rule out this dimension as the cause.
Reading the output
Every finding is severity-classified. The playbook is the same across tools:
- Critical / red — same-week fixes. These block the primary signal and cascade into downstream dimensions.
- Warning / amber — same-month fixes. Drag the score, usually don't block.
- Info / blue — context only. Often what a PR reviewer would flag but that doesn't block merge.
- Pass / green — confirmation. Keep the control in place.
Every audit also emits an "AI fix prompt" — paste into ChatGPT / Claude / Gemini for exact copy-paste code patches tied to your specific stack.
Related tools in this family
- Mega Analyzer — the kitchen-sink audit — surfaces schema health alongside SEO / perf / security.
- Schema Completeness — scores per-type completeness (Article / Product / FAQ / LocalBusiness).
- Rich Results Eligibility Audit — Google-specific eligibility checklist for the major rich-result types.
- Knowledge Graph + Wikidata Entity Audit — Wikidata linkage is the strongest single signal for Knowledge Panel eligibility.
- E-E-A-T Audit — Person + Organization schema + sameAs depth — E-E-A-T pillars meet schema here.
Fact-check notes and sources
- Schema.org: Full type hierarchy
- Google: Structured data general guidelines
- Google Rich Results Test: https://search.google.com/test/rich-results
- Schema Markup Validator: https://validator.schema.org/
This post is informational and not a substitute for professional consulting. Mentions of third-party platforms in the tool itself are nominative fair use. No affiliation is implied.
