Schema markup template and structured data audit
Which JSON-LD exists, whether it validates, whether it matches the page — and the markup you are missing.
- Google Search Console
- Sanity / CMS
- Rich Results Test
- Linear
Audit structured data across our blog, product, and pricing templates.
- Extracted markup from 3 templatesArticle and Organization present; Product and FAQ missing
- Validated against Google requirements2 errors: missing author on Article, invalid date format
- Compared markup to visible contentPricing page markup lists a tier that was removed in June
- Generated missing JSON-LDFAQPage for 14 blog posts, Product for pricing, Breadcrumb sitewide
Two validation errors and one content mismatch — the stale pricing tier is the urgent one, since it markets an offer you no longer sell. FAQ markup on 14 posts is the largest missed opportunity; blocks are generated and ready to paste.
A schema markup template is worth little if the markup contradicts the page it sits on. This audit checks all three things that matter: whether structured data exists, whether it validates, and whether it accurately describes what a reader actually sees — then supplies the JSON-LD for what is missing.
What is a schema markup template?
A reusable JSON-LD pattern for a page type — Article, Product, FAQPage, HowTo, Organization — with the required and recommended properties filled from that page’s real content. The template is per page type, because markup that fits an article is wrong on a product page.
Inventory existing markup
- Extract all JSON-LD, microdata, and RDFa per page template
- Validate against schema.org requirements and Google’s rules
- Flag duplicated or conflicting entity definitions
Check markup against page content
- Confirm FAQ markup matches visible questions and answers
- Catch prices, ratings, and dates that disagree with the page
- Flag markup describing content a user cannot see
Generate the missing JSON-LD
- Produce templates per page type with real values filled in
- Wire entity references so pages link to one Organization
- Prioritize types eligible for rich results
What is the recommended format for implementing schema markup?
JSON-LD in a script tag, which Google states it prefers. It keeps structured data separate from your markup, so changing a heading does not silently break your schema the way inline microdata does.
Which schema types produce rich results?
A limited set: FAQ, HowTo, Product, Review, Breadcrumb, Event, and a handful more. Everything else helps machines understand entities without changing how your result looks — which is still worth doing, and the audit is explicit about which category each type falls into.
Does structured data help with AI search?
It helps answer engines parse claims reliably, especially FAQ and HowTo blocks where the question and answer are explicitly paired. Schema is not a ranking shortcut, but ambiguity is expensive when a machine is deciding what to quote.
How the schema markup template works across your stack
The agent fetches your pages, extracts and validates structured data, and cross-checks Search Console rich-result reports for errors Google has already flagged. Generated JSON-LD arrives as copy-ready blocks per page template, so a developer or CMS editor applies one pattern instead of hand-writing markup per URL.
- Google Search Console
- Sanity / CMS
- Rich Results Test
- Linear
Who uses this schema markup template
- Technical SEOs
- Verify markup validates and matches the page after every template change.
- Developers
- Get copy-ready JSON-LD per template instead of a schema.org reading assignment.
- AEO-focused teams
- Make FAQ and HowTo blocks machine-readable so answer engines can quote them.
How to run this schema markup audit in Metaflow
Give the agent representative URLs
One or two per page template is enough — findings apply to the template, not the URL.
Let it extract and validate
It parses existing markup, validates it, and compares each claim against visible content.
Review errors and gaps
Errors first, mismatches second, missing opportunities third, with rich-result eligibility noted.
Apply the generated JSON-LD
Take the per-template blocks into your CMS or codebase and re-run to confirm.
What you provide
- Representative URLs per page template
- Optional: Search Console access
- Business details for Organization markup
What you get back
- Validation errors and warnings
- Markup-versus-content mismatches
- Copy-ready JSON-LD per template
- Rich-result opportunity list
Why use this schema markup template?
Checks markup against visible content, not just against the spec
Generates JSON-LD per page template rather than per URL
Separates rich-result eligibility from general entity clarity
Cross-references errors Google already reports in Search Console
Schema markup template FAQs
What is a schema markup example?
An FAQPage block listing each question as a Question with its acceptedAnswer text. On this page, the questions you are reading are marked up that way, which is what makes them eligible to be quoted directly.
How do you create schema markup?
Pick the type that matches the page, fill in the required properties from real page content, embed it as JSON-LD, and validate. The failure mode is not syntax — it is markup that describes something slightly different from the page.
Is schema markup still relevant?
Yes, and arguably more so as answer engines parse pages programmatically. Rich results have narrowed for some types, but unambiguous machine-readable claims matter more when a model decides what to cite.
What is the best schema markup generator?
Generators produce syntactically valid markup but cannot tell whether it matches your page — which is the error that actually costs you eligibility. This audit checks that correspondence, then generates the block.
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Key takeaways
- Markup that contradicts the page is worse than no markup
- Generate per template, not per URL, or maintenance collapses
- FAQ and HowTo blocks pay off twice: rich results and AI citation