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Methodology · version 4.2

Methodology 4.2 published ; public criterion catalog updated .

How to interpret technical AI Readiness

Methodology 4.2 evaluates the homepage plus a bounded sample of up to two internal sitemap URLs. The main score covers machine readability, discovery, indexing controls and technical accessibility; a missing sample lowers assessment completeness rather than creating an artificial score penalty. Delivery Reliability remains separate.

This page explains what is evaluated, why each criterion matters, and which part of the assessment it belongs to.

AI Readiness · 0–100

The primary score contains four dimensions with a defensible connection to technical AI readiness. Delivery Reliability is shown separately and does not change AI Readiness.

  • Machine-readable semantics. Structured entities and data that can be interpreted without guessing from prose alone.
  • Discovery and crawler rules. Public discovery surfaces, robots policy and supported search-crawler access. Other content-use policies are shown separately and do not change this dimension.
  • Metadata and indexing controls. Canonical URLs, metadata and explicit indexing directives.
  • Technical accessibility. Successful HTTP delivery and usable content without requiring unsupported client-side execution.

Delivery Reliability · 0–100

HTTPS, redirect safety, HSTS, Content-Security-Policy, framing protection, X-Content-Type-Options, Referrer-Policy, mixed-content and form-action safety are useful web-security signals, but they are reported as a separate reliability index and do not reduce AI Readiness.

Experimental coverage

WebMCP, agent interaction semantics, llms.txt and llms-full.txt are shown separately as optional signals. Their absence does not reduce AI Readiness, and their presence does not guarantee indexing, citation or use by an AI product.

AI-commerce 1.0

AI-commerce is a separate context-gated index. It is evaluated only when a commerce context is confirmed and uses a bounded product sample plus optional agentic discovery signals such as UCP, A2A and OpenAPI.

The checker never performs checkout, payment, order creation, A2A tasks, MCP tools or other state-changing agent operations.

Statuses and completeness

  • Pass: the expected technical signal was observed.
  • Warning: a usable signal exists but has a relevant limitation.
  • Fail: a confirmed contradiction or technical problem was observed.
  • Not evaluated: the available response or context is insufficient for a defensible conclusion.
  • Optional: an experimental or non-required capability is absent or not applicable.

A separate completeness indicator shows how much of the evaluable model was actually assessed. A high score with low completeness should not be interpreted as equivalent to a fully evaluated result.

Criterion catalog

The criteria below use the same definitions as the Russian methodology. Public links point to stable criterion anchors so guides and reports can reference the relevant explanation directly.

Machine-readable semantics

  • Structured data

    AI Readiness

    What is evaluated: Readable Schema.org markup in at least one supported format.

    Why it matters: Structured data exposes entity types, properties and relationships explicitly instead of leaving them to inference from prose.

  • Schema.org JSON-LD

    Diagnostic

    What is evaluated: Syntactically readable JSON-LD blocks using Schema.org vocabulary.

    Why it matters: JSON-LD is a common way to publish structured entities separately from the surrounding HTML markup.

  • Schema.org Microdata

    Diagnostic

    What is evaluated: Schema.org markup embedded in HTML through Microdata attributes.

    Why it matters: Microdata is a valid structured-data format, but it does not need to duplicate an already correct JSON-LD or RDFa representation.

  • Schema.org RDFa

    Diagnostic

    What is evaluated: Schema.org markup embedded in HTML through RDFa attributes.

    Why it matters: RDFa is another valid structured-data format and should be evaluated as an alternative, not as a mandatory companion to JSON-LD.

Discovery and crawler rules

  • Search/discovery crawler access

    AI Readiness

    What is evaluated: robots.txt access for supported search/discovery crawler controls; answer-use, user retrieval and model-use policies are reported separately.

    Why it matters: Search/discovery controls affect whether automated search systems can crawl public content, while training or model-use policy is a separate publisher choice and should not reduce this score.

  • Sitemap discovery in robots.txt

    AI Readiness

    What is evaluated: A valid Sitemap directive published in robots.txt.

    Why it matters: A Sitemap directive gives automated systems a standard discovery path to the site’s published URL inventory.

Metadata and indexing controls

  • Canonical URL

    AI Readiness

    What is evaluated: An absolute canonical URL aligned with the final public document address.

    Why it matters: Canonical metadata helps distinguish the preferred document from duplicates and technical URL variants.

  • Title element

    AI Readiness

    What is evaluated: A non-empty HTML title element.

    Why it matters: The document title provides a concise identity for browsers, search systems and other metadata consumers.

  • Meta description

    AI Readiness

    What is evaluated: A non-empty page description meta tag.

    Why it matters: A description gives machine consumers a concise summary of the page without replacing the primary content.

  • Robots meta

    AI Readiness

    What is evaluated: Indexing directives published in the page-level robots meta tag.

    Why it matters: A noindex directive can prevent search discovery regardless of otherwise strong technical signals.

  • Open Graph metadata

    AI Readiness

    What is evaluated: Core Open Graph properties for the page identity and preview.

    Why it matters: Consistent preview metadata makes the page identity more portable across systems that consume Open Graph.

  • X-Robots-Tag

    AI Readiness

    What is evaluated: Indexing directives published in the final HTTP response headers.

    Why it matters: X-Robots-Tag can restrict indexing independently of the HTML robots meta tag.

  • Internal pages: publication consistency

    AI Readiness when evaluated

    What is evaluated: Sampled sitemap URLs are checked for agreement between their publication signals and preferred page address.

    Why it matters: The sitemap should describe public pages consistently with the pages themselves.

  • Internal pages: basic metadata

    AI Readiness when evaluated

    What is evaluated: The sample checks document title and description presence plus duplicate titles between sampled pages.

    Why it matters: Page identity and concise document context should exist beyond the homepage.

Technical accessibility

  • Homepage response

    AI Readiness

    What is evaluated: A usable public HTML response from the site homepage.

    Why it matters: A failed or non-HTML homepage does not provide a defensible base document for the rest of the technical assessment.

  • robots.txt

    AI Readiness

    What is evaluated: Availability and basic readability of the public robots.txt file.

    Why it matters: robots.txt is the standard publication point for crawl rules and Sitemap discovery.

  • sitemap.xml

    AI Readiness

    What is evaluated: Availability and basic readability of the public XML sitemap.

    Why it matters: A sitemap helps automated systems discover public URLs without implying that those URLs will be indexed or ranked.

  • Document HTTP headers

    AI Readiness

    What is evaluated: An interpretable HTML Content-Type and related document-delivery headers.

    Why it matters: Incorrect response metadata can prevent reliable automated interpretation even when HTML is present.

  • Content without mandatory JavaScript

    AI Readiness

    What is evaluated: Useful page content present in the received HTML without executing external JavaScript.

    Why it matters: Not every automated consumer executes client-side JavaScript before reading a page.

  • Internal pages: accessibility

    AI Readiness when evaluated

    What is evaluated: A bounded sample of up to two sitemap URLs is checked for usable HTML and useful initial content.

    Why it matters: A successful homepage does not prove that internal site pages are equally reachable to automated systems.

Delivery Reliability

  • Redirect chain

    Delivery Reliability

    What is evaluated: The public redirect path, including origin changes and protocol downgrades.

    Why it matters: Redirects determine the actual document URL and can cross trust or transport boundaries before content is reached.

  • HTTPS on the final URL

    Delivery Reliability

    What is evaluated: Delivery of the final page over HTTPS.

    Why it matters: HTTPS protects response integrity and confidentiality between the site and the client.

  • HSTS

    Delivery Reliability

    What is evaluated: Strict-Transport-Security on HTTPS responses.

    Why it matters: HSTS tells supporting browsers to use HTTPS for subsequent requests and reduces downgrade exposure.

  • Content-Security-Policy

    Delivery Reliability

    What is evaluated: A Content-Security-Policy response header.

    Why it matters: CSP constrains allowed resource and execution sources and can reduce the impact of content-injection attacks.

  • Framing protection

    Delivery Reliability

    What is evaluated: Public restrictions on embedding the page in frames.

    Why it matters: frame-ancestors or X-Frame-Options can reduce clickjacking and unwanted framing of the interface.

  • X-Content-Type-Options

    Delivery Reliability

    What is evaluated: The nosniff response directive.

    Why it matters: nosniff tells browsers not to reinterpret resources as a different content type from the declared Content-Type.

  • Referrer-Policy

    Delivery Reliability

    What is evaluated: A policy controlling referrer information sent to other origins.

    Why it matters: Referrer-Policy limits unintended disclosure of source URLs and their parameters to external recipients.

  • Permissions-Policy

    Diagnostic

    What is evaluated: A policy restricting selected browser capabilities.

    Why it matters: Permissions-Policy can reduce unnecessary access to browser features, although it is diagnostic and does not affect AI Readiness.

  • Mixed content in HTML

    Delivery Reliability

    What is evaluated: Explicit HTTP resource references inside an HTTPS page.

    Why it matters: Insecure subresources can be blocked by browsers and weaken the integrity of an otherwise HTTPS page.

  • Form action safety

    Delivery Reliability

    What is evaluated: Forms on HTTPS pages that submit to an insecure HTTP URL.

    Why it matters: Submitting form data over HTTP can expose or alter user input in transit.

Optional machine-readable signals

  • WebMCP readiness

    Optional

    What is evaluated: Statically detectable browser-side WebMCP tool registration.

    Why it matters: WebMCP can expose structured page-level tools to browser agents, but it remains an emerging capability and is not required for AI Readiness.

  • llms.txt

    Optional

    What is evaluated: Availability of the proposed /llms.txt machine-context document.

    Why it matters: llms.txt can provide additional machine-readable navigation context, but its presence does not guarantee discovery, indexing or citation.

  • llms-full.txt

    Optional

    What is evaluated: Availability of an extended proposed LLM context document.

    Why it matters: llms-full.txt can expose more machine-readable context, but it remains optional and does not guarantee use by an AI system.

  • Agent interaction semantics

    Optional

    What is evaluated: Static coverage of native interactive controls, accessible names and selected ARIA roles and states.

    Why it matters: Browser agents can use accessible names, roles and states to interpret actions, but this diagnostic is not a ranking factor and does not replace a browser accessibility audit.

AI-commerce criteria referenced by the English guides

  • Product structured data

    AI-commerce

    What is evaluated: Product or ProductGroup with offer semantics on sampled product pages.

  • Core product fields

    AI-commerce

    What is evaluated: Machine-readable name, description and image coverage.

  • Price and currency

    AI-commerce

    What is evaluated: Machine-readable price paired with a clear currency.

  • Availability

    AI-commerce

    What is evaluated: Machine-readable product availability state.

  • Brand

    AI-commerce

    What is evaluated: Machine-readable brand information where applicable.

  • Product identifiers

    AI-commerce

    What is evaluated: Stable identifiers such as SKU or globally recognized product IDs.

  • Product URL and canonical

    AI-commerce

    What is evaluated: A stable product URL aligned with canonical metadata.

  • Variant mapping

    AI-commerce

    What is evaluated: Machine-readable relationship between product variants and their offers.

  • Visible/JSON-LD price consistency

    AI-commerce

    What is evaluated: Unambiguous visible price and JSON-LD price do not contradict each other.

  • Visible/JSON-LD availability consistency

    AI-commerce

    What is evaluated: Unambiguous visible availability and JSON-LD availability do not contradict each other.

  • UCP profile

    AI-commerce · optional

    What is evaluated: Version-aware validation of a published Universal Commerce Protocol discovery profile.

  • A2A Agent Card

    AI-commerce · optional

    What is evaluated: Supported public Agent Card structure and safe declared interfaces.

  • OpenAPI discovery

    AI-commerce · optional

    What is evaluated: A public machine-readable HTTP contract where agent-facing operations are declared.

  • MCP declaration

    AI-commerce · optional

    What is evaluated: A declared MCP-facing integration surface in the bounded commerce discovery context.

  • Agent endpoint safety

    AI-commerce

    What is evaluated: Declared agent-facing endpoints avoid unsafe local/private/credential-bearing publication patterns.

Version history

  • 1.0 — 3 August 2026. Initial reproducible technical scale.
  • 2.0 — 3 August 2026. Expanded semantics, AI discovery and metadata coverage.
  • 3.0 — 3 August 2026. Stricter context gates, delivery security inside the general technical score and separate experimental coverage.
  • 4.0 — 8 August 2026. AI Readiness separated from Delivery Reliability.
  • 4.1 — 11 August 2026. Crawler-policy handling was clarified and agent interaction semantics were added as optional diagnostics.
  • 4.2 — 14 August 2026. Added bounded internal-page readiness sampling from sitemap URLs.