Blog Schema: JSON-LD Structured Data Generation

Generates complete, validated JSON-LD schema markup for blog posts using the @graph pattern. Combines multiple schema types into a single script tag with stable @id references for entity linking.

Workflow

Step 1: Read Content

Read the blog post and extract all schema-relevant data:

• Title (headline)
• Author (name, job title, social links, credentials)
• Dates (datePublished, dateModified / lastUpdated)
• Description (meta description)
• FAQ section (question and answer pairs)
• Images (cover image URL, dimensions, alt text; inline images)
• Organization info (site name, URL, logo)
• Word count (approximate from content length)
• Tags/categories (for BreadcrumbList category)
• Slug (from filename or frontmatter)

Step 2: Generate BlogPosting Schema

Complete BlogPosting with recommended properties when applicable:

{
  "@type": "BlogPosting",
  "@id": "{siteUrl}/blog/{slug}#article",
  "headline": "Concise post title",
  "description": "Concise page-specific meta description",
  "datePublished": "YYYY-MM-DD",
  "dateModified": "YYYY-MM-DD",
  "author": { "@id": "{siteUrl}/author/{author-slug}#person" },
  "publisher": { "@id": "{siteUrl}#organization" },
  "image": { "@id": "{siteUrl}/blog/{slug}#primaryimage" },
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "{siteUrl}/blog/{slug}"
  },
  "wordCount": 2400,
  "articleBody": "First 200 characters of content as excerpt..."
}

Google's Article structured data docs do not define required Article properties. Include headline, datePublished, author, publisher, and image when applicable, validate with the Rich Results Test, and treat missing fields as warnings unless the target surface requires them. Recommended properties: description, dateModified, mainEntityOfPage, wordCount, articleBody (excerpt).

Step 3: Generate Person Schema

Author schema with stable @id for cross-referencing:

{
  "@type": "Person",
  "@id": "{siteUrl}/author/{author-slug}#person",
  "name": "Author Name",
  "jobTitle": "Role or Title",
  "url": "{siteUrl}/author/{author-slug}",
  "sameAs": [
    "https://twitter.com/handle",
    "https://linkedin.com/in/handle",
    "https://github.com/handle"
  ]
}

Optional properties (include when available):

• alumniOf - Educational institution (Organization type)
• worksFor - Employer (reference to Organization @id if same entity)

Step 4: Generate Organization Schema

Blog's parent organization entity:

{
  "@type": "Organization",
  "@id": "{siteUrl}#organization",
  "name": "Organization Name",
  "url": "{siteUrl}",
  "logo": {
    "@type": "ImageObject",
    "url": "{siteUrl}/logo.png",
    "width": 600,
    "height": 60
  },
  "sameAs": [
    "https://twitter.com/org",
    "https://linkedin.com/company/org",
    "https://github.com/org"
  ]
}

Logo requirements: use a valid crawlable image URL and follow the active Organization and Article documentation for the target surface. Do not invent hard logo dimensions unless the project or current docs require them.

Step 5: Generate BreadcrumbList

Navigation breadcrumb schema showing content hierarchy:

{
  "@type": "BreadcrumbList",
  "@id": "{siteUrl}/blog/{slug}#breadcrumb",
  "itemListElement": [
    {
      "@type": "ListItem",
      "position": 1,
      "name": "Home",
      "item": "{siteUrl}"
    },
    {
      "@type": "ListItem",
      "position": 2,
      "name": "Category Name",
      "item": "{siteUrl}/blog/category/{category-slug}"
    },
    {
      "@type": "ListItem",
      "position": 3,
      "name": "Post Title",
      "item": "{siteUrl}/blog/{slug}"
    }
  ]
}

If no category is available, use "Blog" as the second breadcrumb item with {siteUrl}/blog as the URL.

Step 6: Generate FAQPage Entity Schema (Optional)

Extract Q&A pairs from the blog post's FAQ section:

{
  "@type": "FAQPage",
  "@id": "{siteUrl}/blog/{slug}#faq",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is the question?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "The complete visible answer text."
      }
    }
  ]
}

Google retired FAQ rich results for all sites on 2026-05-07. FAQPage is not a Google rich-result or generative-AI optimization path, and it earns no SEO or AI-readiness credit. Only emit it when a visible FAQ genuinely helps readers, with at least one valid Question and matching visible answer. Do not pad an answer to a target length or add an FAQ solely for markup.

Do not substitute QAPage. Google supports QAPage for a page focused on one question where users can submit answers. Editorial FAQs, support FAQs, and blog Q&A sections do not meet that model.

Step 7: Generate VideoObject (if videos present)

For each YouTube video embedded in the post, generate a VideoObject schema:

{
  "@type": "VideoObject",
  "@id": "{siteUrl}/blog/{slug}#video-{index}",
  "name": "Video title",
  "description": "Video description excerpt (first 200 chars)",
  "thumbnailUrl": "https://img.youtube.com/vi/{videoId}/hqdefault.jpg",
  "uploadDate": "{ISO 8601 date}",
  "contentUrl": "https://www.youtube.com/watch?v={videoId}",
  "embedUrl": "https://www.youtube.com/embed/{videoId}",
  "duration": "PT{M}M{S}S",
  "interactionStatistic": {
    "@type": "InteractionCounter",
    "interactionType": { "@type": "WatchAction" },
    "userInteractionCount": {viewCount}
  }
}

Add each VideoObject to the @graph array. Use #video-1, #video-2 etc. for the @id fragment. Extract video metadata from the embed's noscript fallback or from YouTube Data API if available via blog-google.

Step 7.5: Generate ImageObject

Cover image schema for the post's primary image:

{
  "@type": "ImageObject",
  "@id": "{siteUrl}/blog/{slug}#primaryimage",
  "url": "https://cdn.pixabay.com/photo/.../image.jpg",
  "width": 1200,
  "height": 630,
  "caption": "Descriptive caption matching alt text"
}

Image requirements:

• URL must be crawlable and publicly accessible
• Width and height should reflect actual image dimensions
• Caption should match or closely align with the image alt text
• Preferred dimensions: 1200x630 (OG-compatible) or 1920x1080

Step 8: Validate & Warn

Check per-surface support before recommending schema types:

Type	Google Search status	Valid entity/context use
HowTo	No current Google rich-result experience	Valid schema.org type for genuine how-to content
Dataset	Used by Dataset Search, not general Google Search rich results	Valid only for an actual dataset
QAPage	Supported for one question with user-submitted answers	Do not use for editorial FAQ content
Course	Course list remains distinct from the retired Course Info experience	Use only when the current Course list documentation and visible content match
ClaimReview, SpecialAnnouncement, Course Info, Estimated Salary, Learning Video, Vehicle Listing	Former Google Search experiences; support was retired	May remain schema.org-valid, but never recommend them for Google eligibility
PracticeProblem	Removed from Google Search and its documentation	Do not recommend for Google eligibility
Sitelinks Search Box	No dedicated Google Search visual element	Google generates sitelinks algorithmically

Validation checks:

1. All @id references resolve to entities within the @graph
2. dateModified is equal to or after datePublished
3. headline is concise. Warn when it may truncate or becomes unclear
4. description is concise, page-specific, and not duplicated across posts
5. All URLs are absolute (not relative)
6. Image dimensions are positive integers
7. BreadcrumbList positions are sequential starting from 1
8. If FAQPage is emitted, visible Q&A content exists and includes at least 1 valid Question

Generative AI note: Structured data is not required for Google generative AI search, and there is no special AI schema. Prioritize accurate, visible-content-consistent Article/BlogPosting, Person, Organization, and BreadcrumbList entities. Add ImageObject or VideoObject when the assets exist. FAQPage remains optional reader-facing markup and adds no Google AI advantage.

Step 9: Output

Combine all schemas into a single <script> tag using the @graph pattern:

Security requirement: build the JSON-LD with a real JSON encoder, never string interpolation. Before embedding in HTML, make the JSON text script-safe by escaping closing script sequences and literal less-than characters, for example replace </ with <\/ and < with \u003c. User-controlled fields such as headline, description, author name, image URL, and breadcrumb labels must only enter the block as JSON-encoded values.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@graph": [
    { "@type": "BlogPosting", ... },
    { "@type": "Person", ... },
    { "@type": "Organization", ... },
    { "@type": "BreadcrumbList", ... },
    { "@type": "FAQPage", ... },
    { "@type": "VideoObject", ... },
    { "@type": "ImageObject", ... }
  ]
}
</script>

@graph pattern benefits:

• Single script tag instead of multiple - cleaner HTML
• Entity linking via stable @id references (e.g., author references Person by @id)
• Google and AI systems parse @graph arrays correctly
• Easier to maintain and update as a single block

Output options:

• Embedded HTML - Ready to paste into <head> or before </body>
• Standalone JSON - For CMS schema fields or API injection
• MDX component - If the project uses MDX, wrap in a component

Save the generated schema to the blog post file or to a separate schema file as the user prefers.

Google can process JSON-LD generated by JavaScript when it is present in the rendered DOM. Server-rendered markup is still more portable for non-Google crawlers, but source-only JSON-LD is not a Google requirement. For dynamic markup, validate the rendered URL, confirm the values match visible content, and avoid delayed or failed client requests that leave the rendered DOM empty.