Schema Extractability Checker

What This Checks

The schema checker accepts a homepage URL, extracts title, description, canonical tags, headings, and JSON-LD blocks, then reports whether the page exposes enough structured facts for machine parsing.

The result is meant to reduce avoidable crawl and extraction friction. It does not guarantee LLM inclusion, ranking, indexing, training use, or citation.

What Extractability Means

Extractability is the ability for a crawler or parser to identify the core facts of a page from public HTML. A page is easier to extract when it has a clear title, meta description, canonical URL, visible headings, crawlable body text, and valid structured data. The checker focuses on homepage signals because the homepage often defines the entity and gives crawlers their first context.

Good extractability does not guarantee ranking or citation. It reduces ambiguity so systems can better understand what the page represents.

JSON-LD, Microdata, And Entity Signals

JSON-LD is usually the easiest structured data format to audit and maintain because it is separated from visible markup. The checker extracts JSON-LD blocks and reports detected schema types such as Organization, WebSite, Article, Product, FAQPage, BreadcrumbList, or SoftwareApplication.

Microdata can also express schema, but it is more tightly coupled to HTML structure and can be harder to validate at scale. For most SaaS and tool sites, JSON-LD is the practical starting point.

Common Schema Mistakes

Common mistakes include invalid JSON syntax, stale organization names, missing canonical URLs, schema that describes a different page, FAQ markup that does not match visible content, and product markup used for pages that are not actually products. Another common issue is adding schema while leaving the visible page too thin for a human reader.

Schema should support the page, not replace it. The strongest pages align visible headings, body copy, metadata, canonical tags, and structured data around the same entity and purpose.

How Schema Helps AI Search

AI answer engines still need trustworthy page content, but structured data can make entities and relationships easier to identify. Organization and WebSite schema can clarify who owns the site. Article or Product schema can identify the content type. Breadcrumb and FAQ schema can help parsers understand page hierarchy and answerable questions.

Use the checker to find missing or invalid schema, then confirm the markup with a structured data validator before publishing large changes.

A Practical Schema QA Workflow

Begin with the entity that the page is supposed to represent. A homepage usually benefits from Organization or WebSite schema. A tool page may use SoftwareApplication where appropriate. An article should use Article or BlogPosting only when it is actually editorial content. Avoid adding schema types simply because they are popular; mismatched schema can create more ambiguity than no schema at all.

Next, compare schema properties with visible page content. The name, description, URL, logo, breadcrumbs, and FAQ answers should not contradict the page. If FAQ schema is present, the questions and answers should also be visible to users. If breadcrumb schema is present, each parent item should point to a real URL rather than the current page.

Finally, validate syntax and monitor changes. JSON-LD is easy to break with a missing comma, unescaped character, stale URL, or copied object from another page. Run extraction checks after deployments, template changes, domain migrations, and CMS updates.

Example Input

URL: https://example.com/
Checks: title, meta description, canonical, H1, JSON-LD schema types, visible text signals

Example Output

Detected schema types:
- WebSite
- Organization
- BreadcrumbList

Extractability warnings:
- Organization schema is missing sameAs links
- Homepage has a meta description but no clear product summary near the H1
- FAQ content is visible but not represented in JSON-LD

Common Errors Detected

• JSON-LD is valid syntax but describes the wrong page, brand, or entity.
• Schema has name and URL but lacks description, sameAs, mainEntity, or page-specific details.
• FAQ schema does not match visible FAQ text on the page.
• Important facts appear only in images, scripts, or components that are hard to parse.

Recommended Fix Steps

1. Fix broken JSON-LD syntax, stale URLs, or schema copied from another page.
2. Add page-specific name, description, URL, and mainEntity details where relevant.
3. Align schema with visible headings, body copy, breadcrumbs, FAQ text, and canonical tags.
4. Validate structured data after template, CMS, or deployment changes.

Before Requesting Indexing

Before submitting this page in Search Console, confirm that the page returns 200 on the canonical host, has a self-referencing canonical tag, appears in the reduced sitemap only when it is index-worthy, and contains enough visible text to stand on its own. Check that ad placeholders do not interrupt the main workflow, that structured data matches visible content, and that the page does not claim AI search visibility is guaranteed.

For a new domain, it is better to request indexing for a small group of strong pages than to push every thin route at once. Re-run the relevant AI Index Check tool after publishing and keep a record of changes so future crawler, schema, or content updates can be audited.

Recommended Workflow

1. generate an llms.txt file
2. validate your llms.txt
3. check AI crawler access
4. test schema extractability
5. run an AI citation readiness report

FAQ