No useful agent discovery Link headers were found.

Link headers let automated clients discover API catalogs, documentation, and machine-readable alternates without parsing page markup first.

auth.md response did not match Auth.md v1 agent registration guidance. Content-Type "text/html; charset=utf-8" is not Markdown or text/plain; missing OAuth Protected Resource Metadata discovery guidance; missing OAuth authorization-server metadata guidance; missing agent_verified/user_claimed or claim ceremony guidance.

Human login and signup flows are often opaque to agents. Auth.md gives automated clients a stable registration contract instead of forcing them to scrape docs, automate browser forms, or guess credential flows.

Applicable HTML response is missing an enforcing Content-Security-Policy header.

Content Security Policy reduces the impact of injection bugs by limiting where scripts, styles, frames, forms, and other browser resources can load or execute.

No MCP server card was found at current, transitional, or linked discovery paths.

MCP server cards give agents a stable, public pre-connection discovery surface for server identity, remote transports, protocol compatibility, and safe routing without guessing endpoints.

No WebMCP surface was found to probe.

WebMCP gives agents structured page and browser tool hints so they can understand actions without relying only on screen scraping.

OAuth/OIDC was claimed or referenced, but no registered OAuth/OIDC discovery document was found.

OAuth and OIDC discovery let agents and API clients find authorization, token, and key endpoints programmatically instead of scraping login pages or guessing provider setup.

Best candidate returned Content-Type "text/html; charset=utf-8" instead of text/markdown.

Markdown representations give agents a cleaner page form while preserving normal HTML for browsers and cache-safe representation handling.

No explicit User-agent rules were found for major AI crawler tokens.

AI crawler product tokens have different meanings. Explicit robots.txt groups make training, search, and retrieval access policy auditable for compliant crawler operators.

Generic authentication signals were found, but no OAuth Protected Resource metadata signal was detected.

Protected-resource metadata tells agents which API/resource is locked, which authorization servers can issue tokens for it, and how a 401 challenge maps back to the correct resource.

No A2A Agent Card was found at the current, legacy, or fallback discovery paths.

A2A Agent Cards provide protocol-specific discovery for agent identity, skills, input and output modes, transport bindings, capabilities, and security requirements. Legacy A2A also used /.well-known/agent.json, so scanners must classify the card shape before reporting readiness.

No Agent Skills discovery index was found at the canonical or legacy path.

An Agent Skills index lets agents discover task-specific instructions through a small trusted index, then verify and load only the skill artifacts they need.

No Wildcard-style agents.json file was found.

Wildcard agents.json gives agents workflow-level context beyond plain OpenAPI, including flows, links, examples, and API action structure. It is an emerging OpenAPI-adjacent proposal, so scanners should validate the contract shape without treating it as an A2A or IETF standard.

No Content-Usage or Content-Signal declarations were found.

Content usage preference signals communicate intended downstream AI use separately from robots.txt crawl permission. They can express training and search preferences for compliant systems without replacing access-control rules.

No DNS-AID HTTPS/SVCB records were found under _agents.

DNS-AID lets agents discover index, A2A, and other agent entrypoints before fetching HTTP metadata. HTTPS/SVCB records provide the service-binding substrate, while DNSSEC can authenticate the discovery answer when available.

No RSL declarations were found.

Really Simple Licensing is an emerging rights-expression layer for communicating content usage and licensing terms separately from robots.txt crawl permission.

No TDMRep declaration was found at /.well-known/tdmrep.json.

TDMRep is a W3C Community Group protocol and IANA-registered well-known URI for declaring text and data mining reservation policy on applicable content.

Server URL points to a private network host.

OpenAPI documents let agents understand operations, schemas, authentication, servers, and request/response formats before calling an API.

No evidence-backed, citation-length explanatory paragraph was found.

Generative answer systems work best with visible, self-contained, evidence-backed content, clear entities, trustworthy attribution, structured page sections, and crawler-accessible HTML. These signals improve machine understanding even though they do not guarantee AI citations.

No WebSite entity was found in Schema.org structured data. WebSite.publisher is missing.

Organization and WebSite schema help agents identify the publisher, canonical site identity, logo, and related profiles for attribution.

Frame protection relies only on X-Frame-Options; CSP frame-ancestors is the modern control.

Frame protection blocks hostile sites from embedding pages in deceptive frames, reducing clickjacking and UI redressing risk.

10 section(s) bury or omit a concise opening answer.

Answer engines need visible answer passages, clear question and heading structure, entity definitions, evidence, and trust signals. These signals improve extraction and attribution, but they do not guarantee answer-engine citations or search features.

No named author or publisher identity could be extracted.

Author attribution helps agents cite content responsibly, assess source credibility, and distinguish editorial pages from anonymous marketing copy.

Missing AIO trust signals: author, publisher, freshnessDate.

Google AI Overviews and AI Mode depend on normal Google Search eligibility, snippet permissions, useful visible content, consistent structured data, trust signals, and page experience basics. These signals help Google understand and preview a page, but they do not guarantee selection.

Missing source or trust signals: authorOrOwner, freshnessDate, sourceLinks.

Generative answer systems work best with visible, self-contained, evidence-backed content, clear entities, trustworthy attribution, structured page sections, and crawler-accessible HTML. These signals improve machine understanding even though they do not guarantee AI citations.

No natural question headings were detected. No visible FAQ or Q&A pattern was detected.

Answer engines need visible answer passages, clear question and heading structure, entity definitions, evidence, and trust signals. These signals improve extraction and attribution, but they do not guarantee answer-engine citations or search features.

Missing answer evidence or trust signals: author, publisher, freshnessDate.

Answer engines need visible answer passages, clear question and heading structure, entity definitions, evidence, and trust signals. These signals improve extraction and attribution, but they do not guarantee answer-engine citations or search features.

llms.txt includes unsafe or private links that should not be advertised to agents.

llms.txt is an emerging Markdown convention for giving language-model clients curated context and links before they crawl broadly. Broken, private, or low-signal links make the file much less useful even when it exists.

1 structured-data value(s) were not found in visible page text.

Google AI Overviews and AI Mode depend on normal Google Search eligibility, snippet permissions, useful visible content, consistent structured data, trust signals, and page experience basics. These signals help Google understand and preview a page, but they do not guarantee selection.

Missing or weak extraction signals: summary or key takeaways, FAQ or question-answer section where appropriate.

Generative answer systems work best with visible, self-contained, evidence-backed content, clear entities, trustworthy attribution, structured page sections, and crawler-accessible HTML. These signals improve machine understanding even though they do not guarantee AI citations.

The page mixes structured-data syntaxes. This is allowed when consistent, but a single primary syntax is easier to maintain and debug.

Structured data gives agents explicit entities, relationships, and page meaning that are harder to infer reliably from visual layout alone.