AI access report

richaudience.com

Open to answer engines, no structured data.

9 failing 4 partial 14 passing Scanned 2 hours ago · 2 scans on record

Reach

40 / 40

Whether AI crawlers and answer-engine fetchers are permitted to request the page at all, in robots.txt, in robots directives, and at the edge.

Readability

18.2 / 25

Whether a fetcher that does not execute JavaScript receives the actual content, in markup an extractor can segment.

Structure

4 / 20

Machine-readable markup that states the page's type, entities, canonical URL, and discrete facts instead of leaving them to be inferred.

Attribution

1 / 15

Signals that let an answer engine name the author, date the content, resolve the publisher, and cite it under known terms.

Who is allowed to read this site 0 of 24 answer engines blocked

Crawler	Operator	Uses content for	robots.txt	Live request
GPTBot Crawls content that may be used to train OpenAI's generative AI foundation models.	OpenAI	Model training	allowed	served 200
OAI-SearchBot Indexes pages so they can be surfaced and cited in ChatGPT search results, not for training.	OpenAI	Answer index	allowed	served 200
ChatGPT-User Fetches a page when a ChatGPT user or GPT Action asks for it; user-initiated, so robots rules may not apply.	OpenAI	Live retrieval	allowed	not probed
OAI-AdsBot Visits pages submitted as ChatGPT ads to check policy compliance and ad relevance; not used for model training.	OpenAI	Live retrieval	allowed	not probed
ClaudeBot Collects web content that may contribute to training Anthropic's models; honors Crawl-delay.	Anthropic	Model training	allowed	served 200
Claude-User Retrieves pages on demand when a Claude user's question needs live web content.	Anthropic	Live retrieval	allowed	not probed
Claude-SearchBot Indexes content to improve the relevance and accuracy of Claude's search results.	Anthropic	Answer index	allowed	not probed
anthropic-ai Legacy token widely blocked for Anthropic training; Anthropic now documents ClaudeBot, Claude-User and Claude-SearchBot.	Anthropic	Model training	allowed	not probed
Google-Extended Control token with no user agent of its own; governs Gemini training and grounding use of Googlebot data.	Google	Model training	allowed	not probed
Googlebot Crawls and renders pages for Google Search, Images, Video, News and Discover.	Google	Answer index	allowed	not probed
Googlebot-News Robots token controlling Google News inclusion; crawling itself uses the Googlebot user agents.	Google	Answer index	allowed	not probed
Google-CloudVertexBot Crawls sites at a site owner's request to build Vertex AI agents; no effect on Google Search.	Google	Live retrieval	allowed	not probed
GoogleOther Generic Google crawler used by product teams for one-off fetches such as internal research and development.	Google	Model training	allowed	not probed
Applebot Crawls for Siri, Spotlight and Safari search; falls back to Googlebot rules and ignores Crawl-delay.	Apple	Answer index	allowed	not probed
Applebot-Extended Control token with no user agent; disallowing it excludes crawled content from Apple foundation model training.	Apple	Model training	allowed	not probed
Bingbot Indexes pages for Bing search and the Copilot answers that are grounded in the Bing index.	Microsoft	Answer index	allowed	not probed
msnbot Legacy Microsoft search crawler token still honored alongside bingbot.	Microsoft	Answer index	allowed	not probed
PerplexityBot Indexes and links pages in Perplexity search results; not used to collect foundation model training data.	Perplexity	Answer index	allowed	served 200
Perplexity-User ignores robots Fetches a page for a specific user question; Perplexity documents that it generally ignores robots.txt.	Perplexity	Live retrieval	allowed	not probed
Meta-ExternalAgent Crawls the web to train Meta's foundation AI models and to index content directly into products.	Meta	Model training	allowed	not probed
Meta-ExternalFetcher ignores robots Fetches individual links for agentic AI tasks; Meta documents that it may bypass robots.txt.	Meta	Live retrieval	allowed	not probed
FacebookBot Crawls public pages to improve language models behind Meta's speech recognition technology.	Meta	Model training	allowed	not probed
Meta-WebIndexer Indexes pages so Meta AI can cite and link them in its search answers.	Meta	Answer index	allowed	not probed
Meta-ExternalAds Crawls the web to improve Meta's advertising and other business products and services.	Meta	Model training	allowed	not probed
facebookexternalhit ignores robots Fetches shared links for Facebook, Instagram and Messenger previews; may bypass robots.txt for integrity checks.	Meta	Live retrieval	allowed	not probed
Bytespider ignores robots Downloads content to train ByteDance LLMs and is widely reported to ignore robots.txt directives.	ByteDance	Model training	allowed	not probed
TikTokSpider ignores robots Fetches shared URLs for TikTok link previews and feeds; not expected to follow robots.txt.	ByteDance	Live retrieval	allowed	not probed
Amazonbot Crawls for Amazon product and Alexa answers and may use the content to train Amazon AI models.	Amazon	Model training	allowed	not probed
Amzn-SearchBot Indexes content for Amazon search experiences such as Alexa; does not crawl for generative AI training.	Amazon	Answer index	allowed	not probed
Amzn-User ignores robots Fetches live pages to answer a user's Alexa question; Amazon documents it may not follow all robots.txt rules.	Amazon	Live retrieval	allowed	not probed
CCBot Builds the open Common Crawl web archive, a common source of LLM pretraining corpora.	Common Crawl Foundation	Archive	allowed	not probed
Diffbot Extracts structured page data for Diffbot's knowledge graph, which is licensed to AI customers.	Diffbot	Model training	allowed	not probed
omgili Collects forum, news and blog content that Webz.io sells as web data feeds, including for AI training.	Webz.io	Model training	allowed	not probed
omgilibot Legacy Omgili search crawler token still blocked alongside the current omgili agent.	Webz.io	Model training	allowed	not probed
AI2Bot Collects web text for Ai2's open datasets used to train open language models such as OLMo.	Allen Institute for AI	Model training	allowed	not probed
cohere-ai Retrieves pages to answer user-initiated prompts in Cohere's enterprise AI products.	Cohere	Live retrieval	allowed	not probed
cohere-training-data-crawler Downloads training data for the large language models behind Cohere's enterprise AI products.	Cohere	Model training	allowed	not probed
MistralAI-User Fetches pages on demand so Mistral's Vibe assistant can answer a question with live, cited web content.	Mistral AI	Live retrieval	allowed	not probed
MistralAI-Index Indexes content for Mistral search behind Vibe answers; not used for generative AI training.	Mistral AI	Answer index	allowed	not probed
MistralAI-Training Crawls web content to build datasets for training Mistral's generative AI models.	Mistral AI	Model training	allowed	not probed
DuckAssistBot Crawls pages in real time for DuckDuckGo's cited AI-assisted answers; not used for model training.	DuckDuckGo	Live retrieval	allowed	not probed
YouBot Indexes pages for You.com search results and the AI answers built on that index.	You.com	Answer index	allowed	not probed
PanguBot Collects web content used to train Huawei's PanGu family of large models.	Huawei	Model training	allowed	not probed
Timpibot Crawls pages for Timpi's decentralized index, which is also used as LLM training data.	Timpi	Model training	allowed	not probed
ImagesiftBot Downloads public images plus surrounding text to build ImageSift's searchable image index.	ImageSift (Hive)	Model training	allowed	not probed
Kangaroo Bot Scrapes site content into datasets used to train the Kangaroo LLM.	Kangaroo LLM	Model training	allowed	not probed
SemrushBot-OCOB Crawls pages to feed Semrush's ContentShake AI writing tool.	Semrush	Model training	allowed	not probed
Scrapy Generic scraping framework often used to build AI training datasets; obeys robots.txt only when ROBOTSTXT_OBEY is on.	Zyte (open-source framework)	Model training	allowed	not probed

Two of these columns matter differently. robots.txt is what the site declares. Live request is what actually happened when we sent a real request using that crawler's user agent from a datacentre IP, which is how edge blocking, rate limits and challenge pages show up even when robots.txt looks permissive.

Reach 40 / 40

✓

Answer-engine fetchers are allowed to retrieve and cite this page

All 24 answer-engine fetchers are allowed to retrieve pages for citation.

12 pt

✓

Training crawlers may fetch this path

All 23 tracked training crawlers are allowed.

8 pt

✓

AI user agents receive the same 200 response as browsers

Live requests as 4 AI user agents were served normally.

6 pt

✓

No blanket disallow applies to this path

The wildcard group does not disallow the entire site.

5 pt

✓

robots.txt served as plain text with a 200 response

robots.txt served, 23 bytes, 1 group(s).

3 pt

✓

No Crawl-delay directive constrains fetchers

No Crawl-delay directive.

2 pt

✓

Page is indexable, with no noindex directive

No noindex directive on the homepage.

2 pt

✓

Full-length snippet extraction is permitted

Snippets are not restricted by meta tags.

1 pt

✓

X-Robots-Tag header is absent or permissive

No restrictive X-Robots-Tag header.

1 pt

Readability 18.2 / 25

✗

No <main> or <article> element marks the primary content

No <main> or <article> element, so extractors must guess where the content starts.

Why it matters. Boilerplate removers use `<main>` and `<article>` to decide which subtree is the content and which is navigation, promo, and footer. Without a landmark the extractor guesses by text density and often keeps the nav while dropping part of the body.
Fix. Wrap the page's unique content in exactly one `<main>` element, and use `<article>` for each self-contained item inside it. Put navigation in `<nav>`, site chrome in `<header>` and `<footer>`, and tangential blocks in `<aside>` so they are cleanly separable. Do not nest the content inside a `<div>` whose only meaning is a CSS class.

<body>
  <header><nav><!-- site navigation --></nav></header>
  <main>
    <article>
      <h1>How answer engines fetch your pages</h1>
      <p>Content that should be extracted and quoted.</p>
    </article>
  </main>
  <aside><!-- related links --></aside>
  <footer><!-- legal, contact --></footer>
</body>

Reference

3 pt

Visible text is a small fraction of the HTML payload

Text is 5.4% of the 96 KB document; 2 KB is inline script.

Why it matters. Extractors strip scripts, styles, and wrapper markup before passing text to a model, and a page where content is a small fraction of the payload loses more of it to boilerplate removal. Inline JSON state blobs and deeply nested wrappers also push real text past the truncation limit of a retrieval context.
Fix. Move inline hydration state and large inline scripts out of the document, or fetch them after load instead of embedding them. Flatten wrapper `div` trees and let semantic elements carry the content, and keep utility-class soup out of the article body. Serve the same text without the boilerplate at a stable URL if you need a clean extraction target.

4 pt

Heading levels are missing, duplicated, or skipped

1 H1 and 2 headings total.

Why it matters. Chunkers split long pages on heading boundaries and carry the nearest heading into each chunk's metadata. A page with no `h1`, several competing `h1`s, or levels that skip from `h2` to `h4` produces chunks whose topic labels do not match their text, so retrieval matches the wrong passage.
Fix. Give every page one `h1` that names its subject, then nest `h2` and `h3` without skipping levels. Make each heading describe the section beneath it in words a reader would search for, rather than a label like "Overview". Style with CSS instead of choosing heading levels for their font size, and never use a heading tag for a caption or a button.

<h1>Robots.txt rules for AI crawlers</h1>
<h2>Training crawlers</h2>
<h3>GPTBot</h3>
<h3>ClaudeBot</h3>
<h2>Retrieval and citation fetchers</h2>
<h3>OAI-SearchBot</h3>

Reference

3 pt

No meta description summarises the page

Meta description is 25 characters.

Why it matters. The meta description is a short, author-written summary that retrieval systems index alongside the body and often surface as the preview line beside a citation. Without it the surface generates a summary from whatever fragment it extracted, which may be navigation text.
Fix. Add a `<meta name="description">` of roughly 110 to 160 characters that states what the page answers, written per page rather than per template. Include the concrete nouns a reader would search for, and do not restate the title word for word. Leave it out entirely rather than shipping the same string sitewide.

<meta name="description" content="Which robots.txt rules block AI training crawlers, which block answer-engine fetchers, and how to allow citation while opting out of training." />

Reference

2 pt

✓

Substantive text is present in the server-rendered HTML

772 words of text are present in the raw HTML. Most AI fetchers do not run JavaScript.

9 pt

✓

Title is unique and describes the page in specific terms

Title is 25 characters: "Rich Audience Marketplace"

3 pt

✓

Document language is declared on the html element

Declared language: en.

1 pt

Structure 4 / 20

✗

No JSON-LD structured data found on the page

No JSON-LD structured data on the homepage.

Why it matters. JSON-LD gives an answer engine typed facts (headline, author, dates, price, publisher) without inferring them from prose, and those fields populate the entity record a citation is attached to. With no structured data every attribute has to be guessed from text, and guesses are dropped when confidence is low.
Fix. Add one `<script type="application/ld+json">` block describing the page's primary entity, using the schema.org type that actually fits: `Article` or `NewsArticle`, `Product`, `Recipe`, `Event`, `FAQPage`, or `SoftwareApplication`. Populate the required properties for that type and make every value match visible page content. Prefer JSON-LD over microdata or RDFa, since it is the format the major crawlers document, and render it server-side so non-JavaScript fetchers see it.

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Allow AI answer engines in robots.txt",
  "description": "How to opt out of model training while staying citable.",
  "url": "https://example.com/blog/robots-for-ai",
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "https://example.com/blog/robots-for-ai"
  },
  "image": "https://example.com/images/robots-for-ai.png",
  "inLanguage": "en",
  "datePublished": "2026-02-11T09:00:00-05:00",
  "dateModified": "2026-08-04T14:20:00-04:00",
  "author": {
    "@type": "Person",
    "name": "Dana Reyes",
    "url": "https://example.com/authors/dana-reyes"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Example",
    "url": "https://example.com",
    "logo": {
      "@type": "ImageObject",
      "url": "https://example.com/logo.png",
      "width": 512,
      "height": 512
    }
  }
}

Reference

7 pt

✗

Structured data is too generic for what the page is about

No entity types that answer engines consume.

Why it matters. Answer engines route by entity type: a `Product` node supplies price and availability, an `Article` node supplies author and dates, and a `FAQPage` node supplies question and answer pairs. A generic `WebPage` or `WebSite` node on a product or article page carries none of those fields, so the specific facts stay unavailable.
Fix. Replace bare `WebPage` and `WebSite` nodes with the most specific type that describes the page, and fill the properties that type defines. Use `@graph` to publish several linked nodes on one page, such as an `Article` whose `publisher` points at an `Organization` node by `@id`. Add `BreadcrumbList` for hierarchy and reuse the same `@id` values across pages so the entity resolves to one record.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Product",
      "@id": "https://example.com/products/widget#product",
      "name": "Widget Pro",
      "sku": "WGT-PRO-1",
      "brand": { "@type": "Brand", "name": "Example" },
      "offers": {
        "@type": "Offer",
        "url": "https://example.com/products/widget",
        "price": "49.00",
        "priceCurrency": "USD",
        "availability": "https://schema.org/InStock"
      }
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        { "@type": "ListItem", "position": 1, "name": "Products", "item": "https://example.com/products" },
        { "@type": "ListItem", "position": 2, "name": "Widget Pro" }
      ]
    }
  ]
}

Reference

4 pt

✗

No canonical URL is declared for this page

No rel=canonical, so duplicate URLs split citation signals.

Why it matters. Tracking parameters, trailing slashes, and http and https variants make one document reachable at many URLs. Without `rel=canonical` the retrieval index can hold several near-duplicate records, splitting the signals that rank the page and making the cited link unstable.
Fix. Emit an absolute, self-referential `<link rel="canonical">` on every page, using the exact scheme, host, and path you want cited. Point parameterised and paginated variants at the canonical document, and make sure the canonical URL itself returns 200 rather than redirecting. Keep the canonical, the `og:url`, the sitemap entry, and the JSON-LD `url` identical, since conflicting values are treated as a weak hint and may be overridden.

<link rel="canonical" href="https://example.com/blog/robots-for-ai" />
<meta property="og:url" content="https://example.com/blog/robots-for-ai" />

Reference

3 pt

✗

No reachable XML sitemap is declared

No sitemap.xml and none declared in robots.txt.

Why it matters. A sitemap gives crawlers the URL list and `lastmod` timestamps directly, instead of leaving discovery to link traversal that never reaches pages behind search forms or JavaScript routers. Fetchers use `lastmod` to prioritise recrawls, so fresh content is picked up sooner.
Fix. Publish a sitemap of canonical, indexable URLs and reference it with an absolute `Sitemap:` line in robots.txt. Keep each file under 50,000 URLs and 50 MiB uncompressed, using a sitemap index when you exceed either limit. Set `lastmod` from real content changes rather than the build clock, and exclude redirects, error pages, and non-canonical variants.

<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
  <url>
    <loc>https://example.com/blog/robots-for-ai</loc>
    <lastmod>2026-08-04T14:20:00-04:00</lastmod>
  </url>
</urlset>

Reference

2 pt

✓

JSON-LD parses cleanly with recognised schema.org terms

All JSON-LD blocks parse cleanly.

3 pt

✓

Key facts are available in lists or tables

0 tables, 9 lists, 0 code blocks, 0 question headings.

1 pt

Attribution 1 / 15

✗

No /llms.txt index of canonical pages

No /llms.txt.

Why it matters. `/llms.txt` is a markdown file that points an assistant at the canonical pages for a site, so retrieval does not depend on which page a search happened to return. Its format is fixed: one `#` title, a `>` blockquote summary, then `##` sections of markdown links with short notes.
Fix. Publish `/llms.txt` as `text/plain` markdown: an `#` H1 with the project name, a `>` blockquote summary, optional plain paragraphs of context, then `##` sections whose bullets are `[title](absolute-url): note`. Link the pages you want quoted, put lower-priority links under an `## Optional` section, and prefer URLs that also serve clean markdown. Keep it generated from the same source as your sitemap so it does not drift, and remember it is a hint for assistants, not an access control mechanism.

# Example

> Example publishes reference documentation for the Widget API and guides for
> configuring crawler access.

Prefer the pages below over search results; each URL is canonical.

## Docs

- [Widget API reference](https://example.com/docs/api): endpoints, auth, limits.
- [Quickstart](https://example.com/docs/quickstart): first request in five minutes.

## Policies

- [Crawler policy](https://example.com/legal/crawlers): which agents we allow.

## Optional

- [Changelog](https://example.com/changelog): dated release notes.

Reference

4 pt

✗

No machine-readable author is attached to the page

No author or Person entity, which weakens the authority signals answer engines use.

Why it matters. An `author` property in structured data is what lets an answer engine name a person or organisation as the source and link the byline to a stable profile. A byline that exists only as styled text is not reliably associated with the document during extraction.
Fix. Add an `author` property to the page's `Article`, `BlogPosting`, or `NewsArticle` node, typed as `Person` or `Organization`, with a `name` and a `url` pointing at a real profile page. Give each author a stable `@id` and reuse it across posts so the entity consolidates. Keep the visible byline identical to the structured value, and avoid generic names such as "Admin" or "Staff Writer" where a real author exists.

{
  "@context": "https://schema.org",
  "@type": "BlogPosting",
  "headline": "Allow AI answer engines in robots.txt",
  "author": {
    "@type": "Person",
    "@id": "https://example.com/authors/dana-reyes#person",
    "name": "Dana Reyes",
    "url": "https://example.com/authors/dana-reyes",
    "jobTitle": "Infrastructure Engineer",
    "sameAs": ["https://github.com/danareyes"]
  }
}

Reference

3 pt

✗

No machine-readable published or modified date

No publication or modification dates in structured data.

Why it matters. Answer engines prefer recent sources for questions about current state and use `dateModified` to decide whether a cached copy needs refetching. With no machine-readable date the page is treated as undated and loses to competitors that publish one.
Fix. Publish `datePublished` and `dateModified` in the page's structured data as ISO 8601 values with a timezone offset. Update `dateModified` only when the content actually changes, since bumping it on every deploy trains crawlers to ignore it. Mirror the value in a visible `<time datetime>` element so the rendered text and the metadata agree, and keep the sitemap `lastmod` consistent with it.

<time datetime="2026-08-04T14:20:00-04:00">Updated August 4, 2026</time>

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Allow AI answer engines in robots.txt",
  "datePublished": "2026-02-11T09:00:00-05:00",
  "dateModified": "2026-08-04T14:20:00-04:00"
}
</script>

Reference

3 pt

✗

No Organization entity identifies the publisher

No Organization entity, so the brand is harder to resolve to a known entity.

Why it matters. An `Organization` node with `sameAs` links resolves your site to a single entity across knowledge graphs instead of leaving the publisher name as an ambiguous string. That resolution is what lets an assistant attribute an answer to your brand and reuse your name, logo, and contact details.
Fix. Publish one `Organization` node, usually on the home page, with `name`, `url`, `logo`, and `sameAs` pointing at the profiles that already describe you: Wikipedia or Wikidata, Crunchbase, LinkedIn, and your primary social accounts. Give it a stable `@id` such as `https://example.com/#organization` and reference that `@id` from each page's `publisher` property instead of repeating the block. Add `contactPoint` and `address` when they are public, and keep every value identical to what the site shows.

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "@id": "https://example.com/#organization",
  "name": "Example",
  "legalName": "Example Holdings, Inc.",
  "url": "https://example.com",
  "logo": {
    "@type": "ImageObject",
    "url": "https://example.com/logo.png",
    "width": 512,
    "height": 512
  },
  "sameAs": [
    "https://www.wikidata.org/wiki/Q00000000",
    "https://www.linkedin.com/company/example",
    "https://github.com/example"
  ],
  "contactPoint": {
    "@type": "ContactPoint",
    "contactType": "customer support",
    "email": "support@example.com",
    "areaServed": "US",
    "availableLanguage": ["en"]
  }
}

Reference

3 pt

No machine-readable license or usage terms for the content

No licence declaration, so reuse terms are ambiguous.

Why it matters. A `license` property, or a linked terms page, states the reuse conditions in a place a crawler can read, rather than leaving them to be inferred. Where terms are unstated, some pipelines default to the more restrictive handling, which reduces how much of the text is quoted.
Fix. Add a `license` property to the page's structured data pointing at a specific license URL, such as a Creative Commons deed or your own terms page, and add `rel="license"` on the visible link. Use `usageInfo` for conditions that are not a standard license, such as attribution wording or an API-only clause. State the terms once, at a stable URL, and reference it from every page rather than restating it per template.

<a rel="license" href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Allow AI answer engines in robots.txt",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "usageInfo": "https://example.com/legal/content-reuse",
  "creditText": "Example, crawlcensus research desk"
}
</script>

Reference

2 pt

Evidence raw measurements

robots.txt

Size: 23 bytes
Groups: 1
Sitemaps: none declared

View the file as our crawler received it

User-agent: *
Disallow:

Machine-readable extras

llms.txt: absent
ai.txt: absent
Sitemap: absent
Feeds: none
Schema types: none
Edge: Cloudflare
Final URL: https://richaudience.com/en/
HTML size: 96 KB, 772 words of text

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Track changes on this domain pro

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