Schema Evaluator: Structured Data for AI Comprehension
You don’t just want search engines to crawl your site — you want AI systems to understand it.
Schema markup is the bridge between your content and how large language models (LLMs) like ChatGPT, Bing Chat, Claude, and Perplexity interpret it. It shapes how your content is categorized, summarized, and retrieved — often before the page is even read.
Why This Matters Now:
AI models don’t guess. They use the signals you give them. And schema is the most powerful structural signal you control.
A strong schema strategy can:
-Help your content appear in AI-generated answers
-Increase trust, clarity, and credibility
-Let you pre-define what a page is about, who it’s for, and why it matters
This diagnostic will help you assess:
-Schema coverage and clarity
-Whether your structured data matches real content intent
-How AI-readable your data is
-If you’re testing how schema performs in real-world LLM prompts
-Because if you don’t define your page, AI will — and it might get it wrong.
How to Use This Page
This isn’t a scorecard — it’s a diagnostic. Use it to uncover blind spots in how your structured data is helping (or hurting) your visibility in AI systems like GPT, Claude, Bing, and Perplexity.
✅ Complete it with your SEO, dev, or content teams.
✅ Print it. Talk through it. Track your progress over time.
✅ Focus on clarity, not just code.
As you work through each section, ask yourself:
Does our schema truly describe what the page is and does?
Would an AI model understand it — and trust it enough to cite?
Are we creating structured data just to “pass” validators… or to get quoted in answers?
Every question helps you move from crawlable to comprehensible — and from “ranked” to retrievable.
Below, five sections walk you through the questions you need to ask about your structured data and schema.
1. Schema Coverage: What You Use Today
1. What types of schema are you currently using?
☐ None
☐ Only WebPage or generic types
☐ Article, BlogPosting, or NewsArticle
☐ Product, Service, Organization, or Event
☐ Custom-fit schema for content purpose and format
2. Where is your structured data added or managed?
☐ Not sure
☐ Manually inline in HTML
☐ Added by CMS plugins
☐ Via tag manager or custom scripts
☐ Centrally managed and version-controlled in JSON-LD
3. Are your schema types aligned with page content?
☐ No — we use boilerplate templates
☐ Somewhat, but inconsistently
☐ Mostly aligned for blogs/products
☐ Consistently matched for most core content
☐ Always tailored to match content goals
2. Page Purpose Matching & Main Entity
4. Do your schema types clearly reflect the purpose of the page?
☐ No — we use broad/general schema
☐ Sometimes matched, often vague
☐ Matched for a few page types
☐ Clear for all high-priority pages
☐ Always crystal clear about purpose, format, and use case
5. Do you specify mainEntity or page focus fields?
☐ Never
☐ Only on FAQ/HowTo pages
☐ Sometimes for core pages
☐ Included where applicable
☐ Always clearly defined and validated
6. Do your schemas indicate who the content is for?
☐ No
☐ Some hints in description fields
☐ Included for some services/products
☐ Often tagged with audience context
☐ Consistently clear who each page serves
3. Interpretability & Clarity for LLMs
7. Is your schema readable in plain language?
☐ Mostly internal codes, cryptic IDs
☐ Some readable, some technical
☐ Mixed — depends on the tool or page
☐ Human-readable across key fields
☐ Fully optimized for clarity and natural-language reuse
8. How customized is your schema content?
☐ Boilerplate / copy-paste
☐ Mostly default plugin values
☐ Custom for main page types
☐ Tuned for accuracy and LLM readability
☐ Handcrafted with clear summaries, audience, and value
9. Does your schema explain key content highlights?
☐ No — just title and meta fields
☐ A few properties have descriptive data
☐ Most include clear copy or summaries
☐ Rich with quotable, plain-language fields
☐ Designed to summarize the page even without reading it
4. Testing & Retrieval
How often do you validate your schema?
☐ Never
☐ Only during site launches
☐ When issues arise
☐ Monthly in Google or schema testing tools
☐ After every deploy, including prompt-based testing
Have you tested what AI models do with your schema?
☐ No — didn’t know that was possible
☐ We’ve tried a few prompts
☐ We test core content semi-regularly
☐ We test and refine schema with GPT/Claude
☐ Retrieval testing is built into our optimization loop
Are your schemas helping you show up in AI-generated answers?
☐ Unsure
☐ No visible impact
☐ Occasionally cited in Perplexity or Bing
☐ We’ve seen direct schema-powered summaries
☐ Regularly appear due to schema clarity and strength
5. Strategic Fit & Future Readiness
Is your schema part of a larger AI visibility strategy?
☐ No — purely for SEO
☐ Some interest but not formalized
☐ Strategy under development
☐ Aligned with content goals and content types
☐ Core to our LLM visibility and retrieval roadmap
Do you tailor your schema for different formats (FAQ, HowTo, Product, etc.)?
☐ No
☐ Sometimes, when prompted by tools
☐ For key blog and solution pages
☐ Structured differently by content type
☐ Always matched to intent and format for AI use
Are schema updates part of your content publishing workflow?