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Agent Readability Scanner

Score whether an AI agent can understand a page WITHOUT executing JavaScript. Fetches the URL once, parses the SSR HTML, and grades seven axes deterministically: text content, heading structure, metadata quality, internal links, schema presence, and a heuristic JS-dependency estimate. Returns an agent-perspective summary of what a bot reading raw HTML would actually understand.

Tool
›
https://en.wikipedia.org/wiki/Schema.orgHTTP 200
Agent readability score
94/ 100
14,821 chars texttext/html ratio 0.08JS dep 0.20
Agent-perspective summary

An AI agent reading this page would identify it as "Schema.org - Wikipedia". It can ground answers in approximately 14,821 characters of body text. First 240 chars: "Schema.org - Wikipedia Jump to content Main menu Main menu move to sidebar hide Navigation Main page Contents Current events Random article About Wikipedia Contact us Contribute Help Learn to edit Community portal Recent changes Upload file…".

Detected signals

  • <title>Schema.org - Wikipedia
  • meta description—
  • <link rel=canonical>https://en.wikipedia.org/wiki/Schema.org
  • meta robotsmax-image-preview:standard
  • H1 / H2 / H3 count1 / 7 / 3
  • links (internal / total)225 / 386
  • JSON-LD blocks1

Findings

Text-to-markup ratiocontent.text_ratio

8% of the page is readable text (ratio 0.08); the rest is markup and scripts.

fix Server-render the primary copy so readable text outweighs markup.

JavaScript dependencycontent.js_required_for_primary_content

Heuristic dependency score 0.20/1.00 from SSR text volume, text/script ratio, and JS-required placeholders.

fix Server-render primary content; treat JavaScript as enhancement, not a requirement.

Heading structurecontent.headings_hierarchy

1 H1 · 7 H2 · 3 H3.

fix Use exactly one <h1> and section the page with <h2>s.

Title tagmarkup.title

22 characters: "Schema.org - Wikipedia".

fix Set a unique 30–60 char <title>: "Page topic, Brand".

Meta descriptionmarkup.meta_description

No meta description detected.

fix Add <meta name="description"> with a 120–160 char summary.

Canonical declaredmarkup.canonical

https://en.wikipedia.org/wiki/Schema.org

225 internal linkslinks.internal

Of 386 total links. Agents follow internal links to ground answers.

1 JSON-LD blockstructured_data.jsonld_present

Structured data present; validate types with the structured-data validator.

Result summary

Copyable plain-text summary for sharing with engineers / SEO leads.

AgentSpeed Agent Readability Scanner
Target: https://en.wikipedia.org/wiki/Schema.org
HTTP: 200
Score: 94/100
Text chars: 14821  |  text/html ratio: 0.08  |  JS dep: 0.20

Findings:
  [PASS] 14,821 chars of server-rendered text (content.text_amount)
  [PASS] Title present (metadata.title)
  [WARN] No meta description (metadata.description)
  [PASS] Canonical declared (metadata.canonical)
  [PASS] One <h1> present (headings.h1)
  [PASS] 7 H2 / 3 H3 (headings.h2)
  [PASS] 225 internal links (links.internal)
  [PASS] 1 JSON-LD block (schema.present)
  [PASS] Low JS dependency (js.dependency)

Run again at: https://agentspeed.com/tools/agent-readability-scanner
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Turn this one-off check into a scheduled watch. We re-run it, diff the result, and alert you on a regression through the same channels as your agent alerts. Watches are a paid feature.

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Examples

Click an example to run the tool with pre-filled inputs.

Why this matters for AI agents

Most agent-visibility problems are SSR problems. A site that hydrates everything client-side ships an empty shell to GPTBot and ChatGPT-User; a site that puts the value prop, pricing, and CTAs in the initial HTML response ships a real page. The difference shows up in citation rate.

This tool runs in your browser’s tab and reports what the bot actually receives. No headless browser involved. The JS- dependency score is a heuristic from the SSR HTML, not a real render comparison. Flagged in the result.

Common fixes
Empty shell page
Server-render the hero, value props, and pricing. Treat JavaScript as enhancement; the page should be useful with JS disabled.
Missing or thin <title>
Set a unique 30–60 char <title> per page. Format: "Page topic, Brand". Agents quote titles verbatim.
No JSON-LD
Add Organization + WebSite blocks site-wide; add page-kind schemas (Product, Article, FAQPage) per page. The structured-data validator generates copyable templates.
Multiple H1s
One H1 per page. Multiple H1s confuse agents that pick the first as the topic; the rest are weighted as section headings.
Few internal links
Cross-link related content from the body. Agents follow internal links to ground answers; thin internal linking caps citation breadth.
Linked rubric checks
FAQ
Why no headless browser?
Two reasons. First, the question this tool answers is "what does an agent reading the raw HTML response see?", and running a browser would mask that. Second, headless rendering per request would balloon latency and infrastructure cost. The full AgentSpeed scan does include a headless render pass; this tool focuses on the SSR-side signals.
How is JS dependency calculated?
A 0–1 heuristic. Penalties for: short SSR text content (<800 chars adds 0.4, <2,000 adds 0.15), low text-to-HTML ratio (<5% adds 0.4, <15% adds 0.2), and "needs JavaScript" / "Loading…" placeholder phrases in the body (adds 0.3). Capped at 1.0. Anything above 0.6 is treated as fail.
What does "agent perspective summary" mean?
A deterministic one-paragraph reconstruction of what an AI agent reading the SSR HTML would actually be able to summarise about the page. For thin pages, it admits the agent could not summarise the page; for content-rich pages, it reflects the title, description, and a snippet from the body.
Is this the same as the structured-data validator?
Different focus. The validator checks whether your JSON-LD parses and validates; this scanner checks whether agents can read your page at all. JSON-LD is one of several signals here, alongside text content, headings, and JS dependency.
Run a full agent-readiness scan
Full scan

Score the entire site against 30 agent-readiness checks across discoverability, readability, structured data, actionability, and performance.

Get the full report for en.wikipedia.org →