LLM Friendly Score: Is Your Website Visible to AI Crawlers?

Google still drives most search traffic — but the share is shrinking, fast. ChatGPT, Claude, Perplexity, and Google's own AI Overviews are quietly becoming the layer between users and the open web. If their crawlers can't reach your pages, or can't parse them once they arrive, your business is invisible in the answers they generate.

The LLM Friendly Score is a quick way to find out where you stand. We built a free LLM Friendly Score Calculator that audits any URL against the nine signals that determine whether large language models can actually consume your content. This guide explains what each signal means, why it matters, and how to fix the things holding your score back.

What is an LLM Friendly Score?

An LLM Friendly Score is a 0–100 measure of how easily an LLM crawler — like OpenAI's GPTBot, Anthropic's ClaudeBot, or PerplexityBot — can fetch, parse, and understand a webpage. A high score means the site is well-structured, openly accessible, and rich in semantic signals. A low score means the crawlers are getting blocked at the door or, worse, getting in but seeing nothing useful.

Our calculator runs nine checks across three weighted categories and maps the total to a letter grade:

You can run it on your own site and any competitor's site, side by side, to see who's better positioned for the AI search shift.

The 3 categories explained

Category A: Crawler Access (40 points)

If LLM crawlers can't reach your pages, nothing else matters. This category is the biggest single weight on the score because it gates everything downstream.

1. robots.txt LLM bot policy (15 pts). Many sites have robots.txt files written years before GPTBot existed. We check whether you explicitly allow (or at minimum don't block) the main LLM user agents — GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot, and Bytespider. If you've added a blanket Disallow: / for these crawlers, your content will never enter their training data or their real-time retrieval.

2. /llms.txt file presence (15 pts). llms.txt is an emerging standard — a markdown index at the root of your site that gives LLM crawlers a curated map of your most important pages. Think of it as the AI equivalent of sitemap.xml. Sites with an /llms.txt file tend to get cited more often because the model doesn't have to guess what's worth reading.

3. Sitemap discoverability (10 pts). A sitemap.xml only helps if crawlers can find it. We check that you have one AND that it's referenced in your robots.txt via the Sitemap: directive. Many sites have a sitemap but never tell the crawler where to look.

Category B: Content Accessibility (35 points)

Once a crawler is in the door, can it actually read your content? Most modern frontends are JavaScript-heavy, and many LLM crawlers don't execute JavaScript at all. If your content only materializes after a client-side render, the crawler sees a blank page.

4. No-JS content availability (15 pts). We fetch your raw HTML — no JavaScript execution — and count the words. A minimum of 500 visible words means the page is server-rendered or pre-generated and the crawler can read it. React sites without SSR/SSG, single-page apps, and anything that hydrates client-side will fail this check.

5. Semantic HTML landmarks (10 pts). LLMs use HTML structure as a parsing hint. We look for <main>, <article>, <nav>, <header>, and <footer> tags. A page wrapped entirely in nested <div>s tells the model "this is content of unknown type." A page with proper landmarks tells it "this is the main article, this is navigation, this is metadata" — which dramatically improves comprehension.

6. Text-to-markup ratio (10 pts). We compute the share of your raw HTML that's actually visible text. A ratio above 15% means your content is signal-dense. A ratio below that means the page is mostly boilerplate, tracking scripts, or wrapper markup — and the model has to wade through noise to find anything quotable.

Category C: Content Hygiene (25 points)

These are the basic on-page signals that any technical SEO audit would also flag — but they matter doubly for LLMs because models lean on structure to decide what's important.

7. Heading hierarchy (10 pts). Exactly one <h1> per page, no skipped levels (no <h1> jumping straight to <h3>). Headings are how the model builds an outline of your content. A clean hierarchy gives it a clear table of contents to work from.

8. Image alt-text coverage (10 pts). At least 80% of your images need descriptive alt attributes. LLMs can't see images, so alt text is their only way to know what the visual is conveying. Missing alt text means lost context.

9. Title and meta description (5 pts). Both <title> and <meta name="description"> must be present and populated. These are the first signals the model uses to classify the page.

Why this matters now

Five years ago, "SEO" meant ranking on Google. Today the surface area is fragmented:

• ChatGPT Search queries the live web through Bing and direct fetches
• Claude browses URLs users share and increasingly crawls for its retrieval index
• Perplexity runs its own crawler and cites sources directly in every answer
• Google AI Overviews use a separate retrieval pass governed by the Google-Extended user agent

Every one of these surfaces is governed by the same fundamentals: can the crawler get in, and can it parse what it finds? The brands winning AI visibility right now are the ones that optimized for crawlers two years ago without realizing they were doing it. The ones losing are JavaScript-heavy SPAs with Disallow: / for AI bots and no llms.txt.

LLM-friendly is the new mobile-friendly. In 2015, "mobile-friendly" went from a nice-to-have to a Google ranking signal in less than a year. The same compression is happening with AI crawlability — except this time the gatekeepers aren't one company.

How to use the free LLM Friendly Score Calculator

The tool is free, requires no signup, and returns a full audit in under 10 seconds.

1. Open the LLM Friendly Score Calculator. No login. Just a URL field.
2. Enter your homepage URL (or any specific page — the audit runs on whichever URL you submit).
3. Hit "Analyze." The tool fetches your raw HTML, your robots.txt, your sitemap, and your /llms.txt, then runs all nine checks server-side.
4. Read your score and grade. You'll see the overall number, the breakdown by category, and a pass/fail per check.
5. Fix the failing checks. Each one is annotated with what was missing and a one-line fix. Most issues take minutes, not days.
6. Re-run the audit after deploying fixes to confirm the score moved.

Power tip: run the tool on three competitors before you fix your own site. You'll usually find that a couple of them score in the 30s — which is your opening to leapfrog them on AI visibility while they're still ignoring the channel.

Common gotchas

A few patterns we see over and over when sites score badly:

• Blocking AI bots out of misplaced caution. Many CMS templates ship with a default robots.txt that blocks GPTBot to "protect" the site. Unless you have a content-licensing reason to opt out, this is leaving free distribution on the table.
• Treating /llms.txt as optional. It takes ten minutes to write, and it's the single highest-leverage file you can add to your site for AI search.
• Client-side rendering everything. If you've built a Next.js or Nuxt site, make sure your most important pages are using SSR or SSG, not pure client-side rendering. The score will collapse otherwise.
• Auto-generated alt text that's useless. "image_1234.jpg" is technically alt text but it tells the model nothing. The check passes on presence, but human readers — and the LLM citing you — will notice.

FAQ

Q: What does the LLM Friendly Score actually measure? It measures whether large language model crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, etc.) can fetch, parse, and extract meaningful content from your website. It's a composite of crawler access, content accessibility, and on-page hygiene — nine checks total.

Q: Is the LLM Friendly Score the same as SEO? There's overlap but they're not the same. Traditional SEO optimizes for ranking signals in Google's search results. LLM-friendliness optimizes for whether AI models can ingest your content at all and cite it confidently. A site can rank well on Google and still score poorly with LLMs — especially JavaScript-heavy sites that Google can render but LLM crawlers can't.

Q: Will improving my LLM Friendly Score help with Google rankings too? Mostly yes. The fundamentals — clean HTML, proper headings, alt text, server-rendered content, a well-formed sitemap — are good for both Google and AI search. Fixing your LLM Friendly Score usually nudges your traditional SEO in the right direction too.

Q: Should I block GPTBot to protect my content? That's a strategic call, not a technical one. Blocking AI crawlers prevents your content from being used in training data, but it also prevents you from being cited in AI answers. Most content businesses get more upside from being discoverable than they lose to training-data inclusion. If you do block, do it intentionally — not because the default robots.txt did it for you.

Q: How often should I re-run the audit? Once after every major site change (template overhaul, framework migration, new robots.txt). Otherwise, quarterly is enough. The signals are structural, not volatile.

Q: What's the difference between LLM Friendly Score and AEO Score? LLM Friendly Score is about access — can the crawler reach and parse your content? AEO Score (Answer Engine Optimization) is about citability — once it parses you, are you structured well enough to be quoted in an AI answer? You want both. Try our AEO Score Calculator once you've fixed the LLM-friendliness basics.

Getting started

The fastest way to know where you stand is to just run the audit. It takes seconds and tells you exactly what to fix.

Run the free LLM Friendly Score Calculator now →

If you're building a brand that depends on being found — by humans or by AI — treating LLM crawlability as a first-class concern is no longer optional. The sites that are showing up in ChatGPT and Perplexity answers today aren't the ones with the biggest budgets. They're the ones whose engineering teams quietly made the right choices about robots, rendering, and structure. The calculator tells you which choices yours got right.