Structured data audit tool for SEO

You point it at a sitemap and the tool reads the code of every page. First it identifies the page type - an article, a product, a job posting - and then it shows which schema.org markup that page already carries and which is missing for that type. The analysis reads only your page code, with no AI model calls, which makes it the fastest tool of the set.

What are structured data gaps (schema.org)?

Structured data is the markup in your page code that tells a search engine outright what the page is: an article with an author and a date, a product, a recipe, a job posting. A gap is a type of markup that a given kind of page ought to carry and does not.

Not every absence is a problem - only pairing a page with its type shows what is genuinely missing. That is why the analysis first identifies the page profile and only then compares it with the catalogue of expected schema types.

What do you get?

• A site-wide summary: page count, issue count, the most frequently missing schema types and the distribution of page types.
• A table of every URL with its identified profile, detected and missing markup, and a filter that shows only pages with issues.
• CSV export, and a clear warning when fetch success was low - so you do not draw conclusions from half the data.

Why does structured data matter for AI Search?

Structured data is the only place where you tell a search engine outright what a page is: a product, a recipe, an article with a date and an author, a job posting. Everything else is inference from the text.

In GEO it carries extra weight around credibility signals - authorship and author profile links are read precisely from structured data. A page without it forces the model to guess who stands behind it.

The catch is that implementation usually happens once, at launch, and nobody checks later whether new page types got it too. This analysis shows that across the whole site at once.

How does the analysis work?

1. 1

   Reading the sitemap

   We read the sitemap along with any nested files and skip images and other media. The addresses are checked for availability before the run starts.

2. 2

   Fetching page code

   We fetch the HTML of every URL - unsimplified, because structured data lives in markup that content simplification strips away.

3. 3

   Extracting markup

   We read both JSON-LD (including the bundled form popular plugins emit) and microdata embedded in the HTML. For every piece of markup we find, you see where it came from.

4. 4

   Identifying the page type

   We score the page against 18 profiles - from homepage and article to job posting and contact page. When the signals are too weak it stays “unknown” instead of guessing.

5. 5

   Comparing with the catalogue

   For the identified profile we check which of the 30-plus schema types are missing and which are unnecessary.

6. 6

   Filtering false positives

   A recipe recommendation without an ingredient list, or a job posting without the matching phrasing, is discarded - better to skip than to push wrong markup into your code.

What does it cost?

• 1 credit for analysing the whole sitemap.
• The tool uses no AI models and no external SERP data - it works on your page code, which makes it the fastest of the set.
• A job that ends in an error returns the credit automatically.

Frequently asked questions

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