Why is my structured data showing errors?

The usual culprits: a missing required field, an invalid or misspelled type/property, a JSON-LD syntax error (stray comma or brace), a value in the wrong format (relative URL, bad date), or schema that doesn't match the visible page content. A validator pinpoints which one.

What's the difference between structured data errors and warnings?

Errors mean the markup is invalid or missing something required — fix these, as they can disqualify the page from rich results. Warnings flag recommended fields that are absent; the markup still works, but adding them improves quality and eligibility.

How do I validate structured data?

Paste your JSON-LD or a URL into a schema validator to check required and recommended fields per type, and cross-check with Google's Rich Results Test for search-feature eligibility. The two catch different issues, so run both.

Does invalid schema hurt my SEO?

Invalid schema won't get you penalized, but it silently forfeits the benefit — no rich results, and AI systems can't rely on the metadata. Broken markup is wasted effort, which is why validating before you ship matters.

Common structured data errors (and how to fix each one)

Most structured data problems come down to five recurring errors: a missing required field, an invalid or misspelled type, broken JSON-LD syntax, a value in the wrong format, or schema that contradicts the visible page. None get you penalized — but each silently forfeits the benefit, so your markup is effort wasted until it's valid. Here's how to spot and fix each.

Why broken schema is worse than no schema

Invalid structured data doesn't earn a penalty, but it does something almost as bad: it looks like you've done the work while delivering none of the payoff. No rich-result eligibility, and AI systems that would use your metadata can't trust it. You've spent the effort and gotten zero return. That's why "validate before you ship" is the whole game with schema.

Error 1: Missing required fields

Every schema type has required properties. An Article without headline, a Product without name, a Recipe without recipeIngredient — the type is incomplete and disqualified.

Fix: check the required-field list for your type and fill every one. Our Schema Validator lists exactly which required (and recommended) fields are present or missing for each type it detects.

Error 2: Invalid or misspelled types and properties

@type: "Artical", "BlogPost" instead of "BlogPosting", or a property like "datePublish" instead of "datePublished". Schema.org vocabulary is case-sensitive and exact — a near-miss is treated as unknown and ignored.

Fix: copy type and property names exactly from schema.org (or generate the markup so they're correct by construction). Watch for the common ones: BlogPosting (not BlogPost), datePublished/dateModified, en-gb style codes.

Error 3: JSON-LD syntax errors

A single stray comma, an unclosed brace, or a smart-quote instead of a straight quote breaks the entire JSON-LD block — not just one field. This is the most frequent failure when hand-writing markup.

{
  "@type": "Article",
  "headline": "My post",   // ← trailing comma before } breaks it
}

Fix: validate the JSON itself. Better, don't hand-write it — generate it (see below) so the syntax is always well-formed.

Error 4: Values in the wrong format

The property exists but its value is malformed:

Relative URLs where absolute are required (image, logo, url).
Bad dates — use ISO 8601 (2026-07-18), not July 18, 2026.
Wrong data type — a string where an object is expected (e.g., author: "Jane" instead of author: { "@type": "Person", "name": "Jane" }).

Fix: match each value to the format schema.org specifies for that property.

Error 5: Schema that doesn't match the page

Your markup claims a datePublished or author or price that differs from what's visibly on the page. Search engines flag this as "structured data doesn't match content," and it undermines trust in all your markup.

Fix: the schema must describe what's actually rendered. If the page says "Updated July 2026," the dateModified must agree. This also means schema should be server-rendered — if it's injected by JavaScript after load, validators and crawlers reading raw HTML see a mismatch (or nothing), the same JS blind spot that affects AI crawlers.

Errors vs. warnings

When you validate, you'll see two severities:

Errors — invalid or missing-required. Fix these; they disqualify rich results.
Warnings — recommended fields absent. The markup works, but filling them improves quality and eligibility. Worth addressing, not urgent.

How to validate

Schema Validator (ours) — paste JSON-LD or a URL; it detects the type, checks required/recommended fields, and flags format issues. Fast for catching the five errors above.
Google Rich Results Test — checks whether the markup qualifies for a Google rich result specifically. Different question from validity, so run it too.

The two are complementary: ours tells you if the markup is correct; Google's tells you if it's eligible.

Prevent errors at the source

The cleanest way to avoid errors 1–4 entirely is to not hand-write JSON. Our free generators — Article & HowTo Schema Generator and FAQ Schema Generator — output valid, correctly-typed, syntactically clean JSON-LD from a form. Fill the fields, copy the block, and the only thing left to check is that it matches your page (error 5), which validation confirms.

Bottom line

Structured data errors are almost always one of five things — missing required fields, invalid types, JSON syntax, wrong-format values, or content mismatch — and none are penalties, just forfeited benefit. Generate to prevent the syntax and type errors, validate to catch the rest, and ship only markup that's both valid and matches the page.