Site Structure Checker
Audit your site's URL hierarchy, heading flow, internal linking, and structured data for AI comprehension. LLMs parse site structure to understand topical authority and decide which pages to cite.
Check any URL — results in seconds
About this tool
Why does site structure matter for AI search?
LLMs parse your site structure to understand topical authority — whether your site is the right source to cite on a given topic. Clear URL hierarchies, logical heading nesting, breadcrumbs, and schema markup all contribute to how well AI systems understand what your site covers and at what depth.
What is the ideal URL structure for AI indexing?
Clean, descriptive URLs with 2–3 path segments are best. For example, /blog/ai-seo-guide is better than /p?id=12345. The slug should describe the page topic using the exact language people use when searching. Avoid dynamic parameters, underscores, and excessive depth.
How does heading structure affect AI citations?
LLMs parse heading hierarchies to understand how your content is organized into topics and sub-topics. A well-structured H1 → H2 → H3 hierarchy effectively maps your content to the questions AI is trying to answer. Each H2 should correspond to a distinct sub-question under your main topic.
What schema types should I add for better AI comprehension?
The highest-impact schema types for AI are: Organization (for brand pages), FAQPage (for Q&A content), Article or BlogPosting (for editorial content), HowTo (for step-by-step guides), BreadcrumbList (for hierarchy context), and Product (for product pages). Each one helps AI systems slot your content into the right context.
Does outbound linking help or hurt AI visibility?
Outbound links to authoritative sources help. They corroborate the claims you make in your content and signal to AI that your content is well-researched. Avoid linking to irrelevant or low-quality pages, but don't avoid external links out of fear — strategic outbound linking is a positive signal.