Accessibility Checker:
Scored for AI Agents, Not Just Human Screen Readers
Google, ChatGPT, and Perplexity read your page's accessibility tree - headings, landmarks, ARIA labels, semantic HTML - to extract content and decide what you're about. This audit scores that same structure through the lens of an AI agent trying to navigate your page, not a WCAG compliance checklist.
Your Accessibility Tree Is How AI Agents Actually Read Your Site
An accessibility tree costs roughly 4,000 tokens for an AI agent to process. A full-page screenshot costs roughly 50,000 tokens - about 12.5x more. Agents that browse or act on the web favor the cheaper, structured read whenever they can get it, which means the same heading hierarchy, landmark regions, and ARIA labels that help a screen-reader user are also the signals an AI agent leans on.
Sites with proper semantic structure are cited roughly 3.2x more often in AI-generated responses. Poor structure doesn't just fail an accessibility audit - it fails silently in front of every AI agent that tries to parse the page.
Four Structural Signals AI Agents Rely On
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Heading Hierarchy
A logical H1 through H6 structure with no skipped levels. This is the outline an AI agent uses to understand how your content is organized before it reads a word of body text.
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Landmark Regions
main, nav, header, and footer regions let an agent locate page sections directly. Without them, the agent has to infer structure from generic divs - far less reliable.
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ARIA Labels & Roles
Interactive elements and complex components need explicit labels. Missing ARIA leaves those regions effectively unnamed in the accessibility tree an agent reads.
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Link Text Quality
AI agents read link text as a navigation signal. "Learn about our audit process" tells an agent where that link goes. "Click here" tells it nothing.
Frequently Asked Questions
- How is this different from a regular accessibility checker?
- Most accessibility checkers score WCAG/ADA compliance for human screen-reader users. This tool scores the same underlying structure for a different reader: AI agents. Google, ChatGPT, and Perplexity extract content from your page's accessibility tree, not a rendered screenshot, so the same structural work that helps a screen reader also determines whether an AI agent can reliably parse your page.
- Why does an AI agent care about my accessibility tree?
- An accessibility tree costs roughly 4,000 tokens for an AI agent to process, versus roughly 50,000 tokens for a full-page screenshot - about 12.5x cheaper. Agents that browse or act on the web favor the accessibility tree for exactly this reason. A page with poor heading hierarchy, missing landmarks, or unlabeled ARIA regions gives that agent a much weaker signal to work from.
- What does the audit actually check?
- Heading hierarchy (a logical H1 through H6 structure, not skipped levels), landmark regions (main, nav, header, footer - how an agent locates page sections without guessing from divs), ARIA labels and roles on interactive elements, and link text quality (descriptive text an agent can use as a navigation signal, versus generic text like "click here" that provides none).
- Does fixing this help my SEO too?
- Yes - proper heading hierarchy, landmark regions, and descriptive link text are also core on-page SEO fundamentals. This audit gives you a second, concrete reason to fix them beyond human accessibility: sites with clean semantic structure are cited more often in AI-generated answers, because the agent generating that answer can actually parse what your page is about.