Schema Markup Validator

JSON-LD (JavaScript Object Notation for Linked Data) is Google's recommended format for adding structured data to web pages. Unlike Microdata or RDFa, JSON-LD sits in a<script> tag and doesn't require inline HTML changes, making it easier to implement and maintain.

Valid schema markup enables rich results in Google Search — star ratings, FAQ accordions, product cards, recipe carousels, and more. Invalid schema means missed opportunities: your pages won't qualify for these enhanced SERP features, reducing click-through rates and visibility.

Our validator checks three critical layers: JSON syntax (brackets, commas, quotes), required properties (fields Google expects for each type), and Rich Results eligibility (whether your markup meets Google's guidelines for enhanced search features).

Even experienced developers make schema markup mistakes. Here are the most frequent errors this validator catches:

• Missing @context — Without "@context": "https://schema.org", search engines cannot interpret your structured data.
• Invalid @type — Using a type that doesn't exist in the schema.org vocabulary (e.g., "BlogArticle" instead of "Article") silently fails.
• Missing required properties — Each schema type has required fields. For example, Product requiresname anddescription; omitting them disqualifies rich results.
• Incorrect date formats — Dates must follow ISO 8601 format (e.g., "2026-01-15"). Non-standard formats like "January 15, 2026" cause validation failures.
• Malformed JSON syntax — Trailing commas, missing quotes around keys, or unescaped special characters break the entire JSON-LD block.
• Nested schema issues — Incorrectly structured nested objects (e.g., an author without its own @type) reduce data quality.
• Deprecated types — Some schema.org types have been superseded. Using outdated types means search engines may ignore your markup entirely.

Schema.org structured data plays a growing role in how AI systems understand and surface web content. Beyond traditional search, AI-powered engines and large language models rely on structured data to extract factual information accurately.

• Entity relationships — Schema markup defines clear connections between entities (organizations, people, products), helping AI systems build accurate knowledge graphs.
• FAQ schema for AI citations —FAQPage markup provides direct question-answer pairs that AI systems can cite verbatim in generated responses.
• Organization schema for brand accuracy — Properly structuredOrganization data ensures AI systems represent your brand name, logo, and contact information correctly.
• JSON-LD for factual extraction — AI systems parse JSON-LD because it provides machine-readable facts without the ambiguity of unstructured HTML text.
• Emerging standards like llm-info — Thellm-info proposed standard extends structured data specifically for AI consumption, signaling which content should be prioritized for LLM training and retrieval.

Not all schema types trigger rich results. Here are the types that Google actively supports for enhanced SERP features:

• Article — Displays headline, image thumbnail, publish date, and author in a prominent article card.
• Product — Shows price, availability, star ratings, and review counts directly in search listings.
• FAQPage — Renders expandable question-and-answer accordions beneath your search result.
• HowTo — Presents step-by-step instructions with images, tools, and estimated time in a guided panel.
• Event — Lists event dates, venue, ticket prices, and performer details in an event card.
• Recipe — Shows cooking time, calorie count, star ratings, and ingredient count in a recipe carousel.
• LocalBusiness — Displays business hours, address, phone number, and ratings in the Knowledge Panel and Maps.
• Review — Adds star ratings and review snippets to search results for reviewed items.
• Video — Shows video thumbnails, duration, upload date, and description in video-rich results.
• BreadcrumbList — Replaces the plain URL in search results with a structured breadcrumb navigation trail.