Structured Data (Content-Type Appropriate) SEO Checker

Structured data is how your content speaks machine language. When it is accurate, complete, and matched to the right content type, search systems can understand what your page is, not just which words appear on it. The result: richer snippets, better discovery, and more qualified visitors who know what to expect before they click.

Why content-type appropriate structured data matters

Structured data connects real-world entities and actions to your pages: an article, a product, a recipe, a local business, a video, an event, a FAQ, and more. When you describe these entities with standardized properties, search engines can:

• - Identify what the page is primarily about (not just which keywords it uses).
• - Group related entities together (author → article → organization).
• - Qualify your page for rich results where they are available for that content type.
• - Disambiguate similar names, products, and places.
• - Surface key information (ratings, price, availability, date, duration, steps, questions) directly in search results.

The key is fitness: the structured data must reflect the actual content and intent of the page. Marking a blog post as a product, or a generic article as a medical guideline, might look like a shortcut but it is a strong negative signal. A professional checker must therefore assess not only syntax, but also content-type appropriateness.

Core principles of high-quality structured data

• - Truthful reflection: Mark up only what users can see or reasonably infer from the page itself.
• - Correct type selection: Choose the most specific, accurate schema type that matches the content.
• - Completeness: Provide all required properties and as many recommended ones as are genuinely available.
• - Consistency: Keep structured data aligned with visible text, metadata, and on-page entities.
• - Technical correctness: Valid JSON-LD (or other format), correct nesting, properly formatted values, and stable IDs.
• - Maintenance: Update structured data when content changes (price, availability, date, ratings, versions).

Formats and implementation choices

Modern implementations strongly favor JSON-LD embedded in the HTML document, because it is easier to maintain, less fragile than markup tied to HTML attributes, and more flexible for multiple entity graphs.

• - JSON-LD: Recommended for most new implementations; easy to generate server-side or client-side (but avoid delayed rendering where possible).
• - Microdata/RDFa: Can still be valid, but tightly coupled to markup structure and more error-prone during design changes.
• - Multiple graphs: A page can contain multiple structured data blocks (e.g., Article + Breadcrumb + FAQ) as long as they are consistent and relevant.

Your checker should accept valid JSON-LD, recognize microdata/RDFa where present, but encourage JSON-LD for maintainability and clarity.

Mapping structured data to page content types

Different kinds of pages call for different schema types. A robust checker first infers the primary content type, then evaluates whether the markup matches it.

Articles, News, Blog Posts

• - Signals: H1/H2 pattern, byline, date, body text, category as article/news/blog, often long-form content.
• - Core properties: headline, description, image, author, datePublished, dateModified, mainEntityOfPage.
• - Enhancements: articleSection, wordCount, about, mentions, thumbnailUrl.
• - Checker focus: Does the page look like an article, and is the article markup present and consistent with title, author, and dates?

Products & Offers

• - Signals: Product name, price, add-to-cart or purchase call-to-action, SKU, variations, reviews, or ratings.
• - Core properties: name, image, description, sku, brand, offers (price, priceCurrency, availability, url).
• - Enhancements: aggregateRating, review, gtin codes, color, size, material.
• - Checker focus: Are product and offer data present and accurate? Is price visible on the page? Is the availability status truthful? Are multiple variants modeled correctly?

Local Business

• - Signals: Physical address, map, opening hours, phone number, service area, local intent.
• - Core properties: name, address, telephone, openingHoursSpecification, url.
• - Enhancements: geo coordinates, sameAs profiles, priceRange, areaServed.
• - Checker focus: Does the markup match a real business location or service area clearly shown on the page? Are hours, address, and phone correct and formatted properly?

Events

• - Signals: A specific date/time, venue, ticketing information, event name and description.
• - Core properties: name, startDate (and endDate if applicable), location, description.
• - Enhancements: offers, organizer, performer, eventStatus, eventAttendanceMode.
• - Checker focus: Are dates valid and in the future or correctly labeled if past? Is the location explicit and consistent? Does the page genuinely describe an event?

FAQ & HowTo

• - FAQ signals: A list of questions with direct answers clearly separated.
• - HowTo signals: Step-by-step instructions with ordered steps, tools or materials, and an outcome.
• - Core properties: For FAQ, question/acceptedAnswer pairs; for HowTo, step, name, and text or images per step.
• - Checker focus: Do questions and answers exist visibly, and do they match the structured data? Are steps presented clearly on-page, not only in JSON?

Videos

• - Signals: Embedded video player, title, description, duration, and sometimes transcript.
• - Core properties: name, description, thumbnailUrl, uploadDate, duration, contentUrl or embedUrl.
• - Checker focus: Is the video actually present and playable? Are title, description, and duration aligned with what users see?

Other common types

• - Organization & WebSite: Best used on key pages (home, about) to define brand identity, logo, contact points, search box behavior.
• - Breadcrumb: Mirrors on-page breadcrumb navigation; clarifies hierarchy.
• - JobPosting, Course, Recipe, SoftwareApplication, Service, Review: Each with its own required set of properties that must match page reality.

Consistency between structured data and visible content

Structured data should never contradict what users see. A professional checker must cross-compare markup with actual page content.

• - Title & headline alignment: headline/name in structured data should be very close to the visible H1 or product name.
• - Dates and prices: datePublished, dateModified, startDate, endDate, price, and availability must match on-page text.
• - Ratings and reviews: aggregateRating and review counts must reflect real, visible reviews on the page.
• - Media: image and thumbnailUrl should refer to actual images used on the page.
• - Entity relationships: author, publisher, brand, and organization names should match visible labels and site-level information.

Anti-patterns to avoid

• - Marking up invisible content: Only content that users can access on the page should be represented in structured data.
• - Wrong type for richer display: Mislabeling content to chase prominent rich results (e.g., turning a generic list into a FAQ).
• - Copy-paste markup: Using identical structured data across multiple pages with different topics or products.
• - Overlapping conflicting types: Using multiple mutually inconsistent types for the same entity.
• - Invalid values: Incorrect date formats, currency codes, URLs, or enumerations (e.g., using free text where a defined value is expected).

Implementation rubric for a Structured Data (Content-Type Appropriate) SEO Checker

This rubric converts best practices into measurable checks. In your tool, “chars” can store character counts or snippet samples, and “pts” represents points contributing to a 100-point score.

1) Type Detection & Fitness — 20 pts

• - Checker infers primary page type (e.g., article, product, local, event, FAQ, video) from layout, headings, and content.
• - Structured data type(s) match the inferred content type.
• - No obviously inappropriate types detected (e.g., JobPosting on a simple blog post).

2) Completeness & Required Properties — 20 pts

• - All required properties for the declared type are present.
• - Key recommended properties are present when reasonably available.
• - Nested entities (Offer, AggregateRating, Place, Organization, Person) are used correctly.

3) Consistency with Visible Content — 20 pts

• - headline/name closely matches the visible title (difference measured via string similarity on chars).
• - Prices, dates, ratings, availability, and key descriptors align with text users see.
• - No evidence of markup for data that does not appear on the page.

4) Syntax & Technical Quality — 15 pts

• - JSON-LD blocks are valid: correct braces, brackets, commas, strings.
• - Types and properties use recognized names from the vocabulary.
• - Values have correct format (e.g., ISO 8601 dates, numeric price, valid URLs).
• - No duplicate IDs or conflicting graphs within a single page.

5) Multi-Type Harmony & Nesting — 10 pts

• - Multiple schema types on the same page are logically related (Article + Breadcrumb + FAQ, Product + Offer + AggregateRating, etc.).
• - Relationships are modeled via @id or nested objects, not loosely scattered copies.
• - No contradictory properties between different graphs about the same entity.

6) Maintenance & Freshness — 10 pts

• - dateModified reflects recent updates where visible content has changed.
• - Event dates, job posting validity, availability, and pricing are not obviously outdated.
• - Structured data changes keep pace with on-page changes.

7) Scope & Appropriateness — 5 pts

• - No excessive markup of minor elements purely to increase markup volume.
• - Only genuinely valuable entities are modeled (main content, not trivial side widgets).

Scoring Output

• - Total: 100 pts
• - Grade bands: 90–100 Excellent, 75–89 Strong, 60–74 Needs Revision, <60 Critical Fixes
• - Diagnostics: Return detected content type, list of schema types, missing required/recommended properties, key value mismatches, syntax errors, and a compact summary of graphs with their chars length.

Diagnostics your checker can compute

• - Content-type inference: Use headings, templates, and keywords (e.g., “add to cart,” “event date,” “FAQ”) to infer page type.
• - Graph extraction: Parse all JSON-LD blocks; map entities by @id and type; list them with a short description.
• - Property coverage: For each type, show which required and recommended properties are present or missing.
• - Value comparison: Compare key values (name, price, date, rating) against visible text snippets (stored in chars samples).
• - Syntax & vocabulary checks: Flag unknown properties, invalid enums, and malformed JSON.
• - Freshness scan: Detect obviously stale dates (past events without context, expired jobs without closing notes).
• - Conflict detection: Highlight any conflicting values for the same entity across multiple graphs.

Example JSON-LD snippet (generic article)

{
  "@context": "https://schema.org",
  "@type": "Article",
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "https://example.com/example-article"
  },
  "headline": "Example Article Title Aligned with H1",
  "description": "Short summary that matches the on-page introduction.",
  "image": [
    "https://example.com/images/example-hero.jpg"
  ],
  "author": {
    "@type": "Person",
    "name": "Author Name"
  },
  "datePublished": "2025-04-15",
  "dateModified": "2025-04-18",
  "publisher": {
    "@type": "Organization",
    "name": "Example Publisher",
    "logo": {
      "@type": "ImageObject",
      "url": "https://example.com/images/logo.png"
    }
  }
}

A checker should verify that the URL, headline, description, author, dates, and publisher all match visible content and site-level identity.

Editorial & technical workflow for durable structured data

1. - Define page models: For each template (article, product, local page, event, etc.), select the appropriate schema type and required properties.
2. - Wire into CMS: Generate JSON-LD automatically from structured fields (title, author, price, schedule) instead of manual snippets wherever possible.
3. - Align design & data: Ensure every marked-up property is visible or clearly implied on the page layout.
4. - Automate validation: Run your checker on every deployment or content publish; block or warn on serious issues.
5. - Maintain mappings: When you add new content types, update the rules and checklists for structured data.
6. - Review high-value pages: Periodically audit key URLs by hand to ensure that automated markup still reflects reality.

Common issues and quick fixes

• - Missing required properties: Identify gaps per content type; add corresponding fields in your CMS and JSON-LD templates.
• - Mismatched values: When headlines, prices, or dates differ, treat the page itself as the source of truth and correct the markup.
• - Wrong type selected: Replace generic or misleading types with the correct one and adjust properties accordingly.
• - Outdated markup on updated pages: Whenever content is refreshed, ensure dateModified and any changed values in structured data are updated too.
• - Syntax errors from manual edits: Favor automated generation and minimal manual editing; when manual edits are necessary, validate JSON before publishing.

Final takeaway

Structured data is most powerful when it is precise, honest, and tailored to the real content type of each page. Choosing the right schema, filling in required and recommended properties, aligning values with visible content, and keeping everything up to date transforms markup from a technical checkbox into a strategic asset. Build your Structured Data (Content-Type Appropriate) SEO Checker to reward fitness, completeness, consistency, and maintainability. When your data layer truly matches your content, search systems can represent you accurately—and the visitors you attract will be the ones your pages are designed to serve.