Schema markup validation is confusing - which tools actually work and what errors matter?

Let me clarify the validation landscape:

The tools and their purposes:

Why results differ:

Rich Results Test:

• Only validates schema that triggers rich results
• Shows Google-specific requirements
• May ignore valid schema that’s not rich-result eligible

Schema Markup Validator:

• Validates against Schema.org vocabulary
• More comprehensive coverage
• Doesn’t check Google-specific requirements

The workflow:

Step 1: Rich Results Test Check if you’re eligible for rich results. Fix any errors shown here first.

Step 2: Schema Markup Validator Validate against Schema.org standards. Catch additional issues.

Step 3: Search Console Monitor ongoing compliance. Spot site-wide problems.

Both tools should pass for confidence.

Common syntax errors and how to fix them:

Error 1: Wrong quotation marks

Bad:

"name": "Article Title"

(These are curly quotes from Word/Google Docs)

Good:

"name": "Article Title"

(These are straight quotes)

Fix: Never write JSON in Word. Use code editor.

Error 2: Missing or extra commas

Bad:

{
  "name": "Title",
  "author": "John"
  "date": "2025-12-31"
}

(Missing comma after author)

Good:

{
  "name": "Title",
  "author": "John",
  "date": "2025-12-31"
}

Error 3: Trailing comma

Bad:

{
  "name": "Title",
  "author": "John",
}

(Comma after last property)

Good:

{
  "name": "Title",
  "author": "John"
}

Error 4: Unbalanced brackets

Count opening and closing brackets. Every { needs matching }. Every [ needs matching ].

Pro tip: Use a JSON validator like jsonlint.com first. Catches syntax before schema-specific issues.

FAQ schema is most common - here’s how to validate it correctly:

Required structure:

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "What is the question?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "The complete answer text here."
    }
  }]
}

Common FAQ schema errors:

1. Content mismatch: Schema Q&A must match visible page content. Can’t have schema-only Q&A.

2. Hidden content: FAQ content behind tabs/accordions may not validate. Some implementations require JS rendering.

3. Multiple FAQ schemas: Usually one FAQPage per page. Multiple can confuse validators.

4. Missing Answer text: Every Question needs acceptedAnswer. acceptedAnswer needs text property.

Validation checklist for FAQ:

• @context and @type correct
• mainEntity is an array
• Each item has @type: Question
• Each has name (the question)
• Each has acceptedAnswer with text
• Content matches page content
• Content is visible (not hidden)

Testing:

1. Rich Results Test with live URL
2. Check “FAQ” rich result type appears
3. Verify preview shows correctly

Article schema tips for AI visibility:

Essential Article schema:

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Your Article Title",
  "datePublished": "2025-12-30",
  "dateModified": "2025-12-31",
  "author": {
    "@type": "Person",
    "name": "Author Name",
    "url": "https://yoursite.com/author/name"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Your Company",
    "logo": {
      "@type": "ImageObject",
      "url": "https://yoursite.com/logo.png"
    }
  },
  "image": "https://yoursite.com/image.jpg"
}

Why each property matters for AI:

Common Article errors:

1. Missing author: Google recommends but doesn’t require. AI systems use for credibility.

2. Date format wrong: Must be ISO 8601: “2025-12-31” Not: “December 31, 2025”

3. Publisher logo missing: Required for some rich results. Should be valid URL to actual image.

4. dateModified older than datePublished: Logically impossible. Systems may flag or ignore.

The AI connection: Good Article schema helps AI understand and trust your content. Author and date are especially important for citations.

How to validate schema at scale:

For large sites (100+ pages):

Option 1: Screaming Frog

• Crawl entire site
• Check “Structured Data” tab
• Export errors/warnings
• Filter by error type

Option 2: Search Console

• Enhancements reports
• See all pages with schema
• Grouped by error type
• Prioritize high-impact fixes

Option 3: Custom scripts

• Extract schema from pages
• Validate programmatically
• Generate reports

Prioritization framework:

Template issues are critical: If your blog template has schema error, Every blog post has that error. Fix template = fix hundreds of pages.

Our workflow:

1. Monthly Screaming Frog crawl
2. Export structured data report
3. Identify template vs. individual issues
4. Fix templates first
5. Batch individual fixes
6. Verify in Search Console

How schema specifically helps AI visibility:

Why schema matters for AI:

1. Explicit structure: AI systems don’t have to guess. Schema tells them exactly what content is.

2. Relationship clarity: Author → Article → Publisher AI understands the connections.

3. Data extraction: FAQPage = clear Q&A pairs. AI can extract and cite accurately.

Schema types most valuable for AI:

Our testing: Pages with schema vs. without schema. Same content, same structure. Schema pages: 35% more AI citations.

The caveat: Schema alone doesn’t guarantee citations. Content quality still primary. Schema helps AI understand quality content.

Validation for AI: Focus on FAQPage and Article schema. Ensure clean, error-free implementation. Test that content matches schema.

My complete validation workflow:

Pre-publish validation:

Step 1: JSON syntax check Use jsonlint.com Catches basic syntax before deployment.

Step 2: Schema Markup Validator Paste code at validator.schema.org Check Schema.org compliance.

Step 3: Rich Results Test Test with Google’s tool. Verify rich result eligibility.

Step 4: Preview check See how rich result will appear. Ensure it looks correct.

Post-publish validation:

Step 1: Test live URL Rich Results Test with actual URL. Confirms schema renders correctly.

Step 2: Search Console monitoring Wait 2-3 days for indexing. Check Enhancements reports.

Step 3: Rich result appearance Search for page. Verify rich results appear.

Ongoing monitoring:

• Weekly: Check Search Console for new errors
• Monthly: Screaming Frog crawl
• Quarterly: Full schema audit

Quick checklist:

• JSON syntax valid
• Schema.org vocabulary correct
• Google requirements met
• Content matches schema
• Renders on live page
• Search Console clean
• Rich results appearing

Quick fixes for common validation issues:

“Missing required property” Check which property is required for your schema type. Add the missing property. Google’s documentation lists requirements.

“Invalid property value” Usually date format or URL issues. Dates: Use ISO 8601 (2025-12-31) URLs: Use full absolute URLs (https://…)

“Property not recognized” Typo in property name. Check exact spelling at schema.org. Case-sensitive: “datePublished” not “DatePublished”

“Invalid JSON-LD syntax” Run through jsonlint.com. Check quotes, commas, brackets. Compare to working examples.

“Content not on page” Schema references content not visible. Either add content or remove from schema. Can’t have invisible schema content.

“Multiple items on same page” Usually fine, but check organization. Use @graph for multiple items if needed. Ensure each has complete required properties.

Tool for debugging: Chrome extension: “Structured Data Testing Tool” Right-click any page to test instantly.

When stuck: Compare to Google’s examples. Strip back to minimal valid schema. Add properties back one by one.

Much clearer now. My validation workflow:

Pre-publish:

1. Write schema in code editor (straight quotes)
2. Validate JSON syntax (jsonlint.com)
3. Check Schema.org compliance (validator.schema.org)
4. Test Google eligibility (Rich Results Test)
5. Preview rich result appearance

Post-publish:

1. Test live URL in Rich Results Test
2. Wait for indexing
3. Check Search Console Enhancements
4. Verify rich results in search

Error priority:

• Parsing errors = fix immediately
• Missing required = fix immediately
• Invalid values = fix promptly
• Warnings = evaluate if applicable

For my site:

• Focus on FAQPage and Article schema
• Use templates for consistency
• Monthly Screaming Frog audit
• Track AI citations for correlation

Key insight: Errors must be fixed. Warnings are optional based on content. Multiple tools give complete picture.

Thanks for demystifying schema validation.