Schema markup checker

What the schema markup checker does

This tool scans a webpage for JSON-LD structured data blocks, lists the schemas it finds, and checks whether each block contains JSON that can be parsed. It is a quick first pass before deeper validation in dedicated structured data testing tools.

JSON-LD is a format for embedding structured data in a script element with the type application/ld+json. Search engines use this data to understand entities on a page: articles, products, organizations, events, breadcrumbs, and more. Well-formed markup can enable rich results and helps machines interpret content with less ambiguity.

Why structured data matters

Structured data describes what a page is about in a machine-readable way. Instead of inferring from prose that a page is a recipe or a product listing, a crawler can read explicit properties such as name, price, rating, or publication date. This reduces ambiguity and, when the markup meets a search engine's requirements, can make a result eligible for enhanced presentation in search.

Structured data also supports consistency across platforms. The same JSON-LD can inform search engines, social platforms, and other consumers that understand schema.org vocabulary. That makes correctness important: malformed or misleading markup can lead to warnings or missed opportunities.

How to read the results

The tool numbers each schema block it finds and shows whether the JSON parses. A block marked invalid contains a syntax error, such as a missing comma, an unquoted key, or a trailing comma. Syntax errors prevent any consumer from reading the data, so they should be fixed first.

A valid JSON block is not automatically correct markup. Parsing only confirms that the JSON is syntactically sound. It does not confirm that the properties match schema.org definitions, that required fields are present for a given type, or that the values are accurate. After fixing syntax, review the content of each block against the vocabulary and the requirements of the search features you are targeting.

Common structured data mistakes

• Invalid JSON syntax. Missing commas, unescaped quotes, and trailing commas are frequent in hand-edited markup.
• Mismatched data. Markup that claims ratings, prices, or availability not visible on the page can violate search engine guidelines.
• Missing required properties. Each schema type has expected fields; omitting them weakens the markup's usefulness.
• Duplicate or conflicting blocks. Several blocks describing the same entity with different values create ambiguity.
• Wrong type selection. Using a generic type when a more specific one fits reduces the value of the markup.

Practical workflow

Run the checker on key templates: articles, product pages, category pages, and the homepage. Fix syntax errors immediately, then validate the parsed content in a dedicated testing tool that checks schema requirements. Keep markup in sync with visible page content, and re-check after template changes, since a broken block can silently persist long after a deployment.

Limitations

The checker focuses on JSON-LD blocks present in the HTML response. It does not validate microdata or RDFa, and it does not confirm eligibility for any specific rich result. Use it as a fast syntax and discovery check, not as a substitute for full validation.