CanAgentUse tools

UCP Suite

Validate Universal Commerce Protocol discovery, then test product search, carts, checkout links, and merchant handoff flows.

MCP Playground

Connect to remote MCP servers, inspect tools and resources, test prompts, auth, headers, notifications, and JSON-RPC responses.

A2A Playground

Inspect Agent Cards, validate advertised endpoints, and prepare safe requests for agent-to-agent workflows.

Agent Website Viewer

Enter a public URL and see the roles, names, landmarks, controls, and blockers that shape how AI agents understand the page.

SCANNED
Jul 3, 2026, 3:01 AM
VISIBILITY
Public
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60/100

OVERALL SCORE

Level 3, Bot-Aware

Moderate readiness for AI agents
AIDiscoverability41%Agent Easeof Use62%Security& Trust54%GEO, AIO, AEO64%SEO100%Performance76%Accessibility79%
  • AI Discoverability 41 out of 100
  • Agent Ease of Use 62 out of 100
  • Security & Trust 54 out of 100
  • GEO, AIO and AEO 64 out of 100
  • SEO 100 out of 100
  • Performance 76 out of 100
  • Accessibility 79 out of 100

CAPTURED SCREENSHOT

Captured website desktop screenshot

What AI sees of your website

Discover and Shop What's Next | Highsnobiety favicon

Discover and Shop What's Next | Highsnobiety

Highsnobiety is a global fashion and media brand passionate about product and the stories that shape them. Discover and shop what's next.

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Detailed report scores grouped by capability area
MetricScoreStatusPassedFailedWarningEvidence
AI Discoverability
41
Priority fix1572View details
Discoverability
67
Needs work800
Content Readiness
9
Priority fix662View details
Bot Access Control
45
Priority fix110View details
Agent Ease of Use
62
Needs work400
API
67
Needs work200
Google Agentic Browsing
100
Strong200
GEO, AIO and AEO
64
Needs work800
GEO Readiness
Not Applicable
Not Applicable200
AIO Readiness
Not Applicable
Not Applicable300
AEO Readiness
Not Applicable
Not Applicable300
SEO
100
Strong1000
SEO
100
Strong1000
Security & Trust
54
Needs work1000
Security & Trust
54
Needs work1000
Performance
76
Mostly ready1700
Performance
76
Mostly ready1700

Prioritized recommendations

Issues ranked by score impact

3 items need attention

AI DiscoverabilityBot Access ControlEstablished

AI bot rules in robots.txt

AI bot rules in robots.txt failed at "Classify AI crawler rules".

69 Fail

Needs attention

AI bot rules in robots.txt

Failed check
01

Issue

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

Details

02

Why it matters

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

Check name

AI bot rules in robots.txt

Score

31/100

Status

fail

Category

Bot Access Control

Maturity

Established

Goal

Declare deliberate robots.txt rules for major AI training, AI search, user-triggered, and dataset crawlers.

Result

AI bot rules in robots.txt failed at "Classify AI crawler rules".

Validation steps

  1. Classify AI crawler rules

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

    robots.txt lacks explicit AI crawler rules
Evidence log1 step · 6 lines
Classify AI crawler rules [fail]! No explicit User-agent rules were found for major AI crawler tokens.INFOClassify AI crawler rulesINFOParsing User-agent groups and Allow/Disallow records for known AI crawler tokens evaluatedPath="/"INFOEvaluating exact User-agent matches before wildcard fallback exactAiPolicyCount=0 totalCrawlerTokens=18FAILNo explicit AI crawler User-agent groups were found examplesExpected=["GPTBot","OAI-SearchBot","ClaudeBot","Google-Extended","CCBot"]FAILCompare explicit AI crawler coverage actual=0 expected="> 0 explicit non-search AI crawler policies" missingTokens=["GPTBot","OAI-SearchBot","ChatGPT-User","ClaudeBot","Claude-SearchBot","Claude-User","Google-Extended","Applebot-Extended","Amazonbot","Amzn-SearchBot","Amzn-User","PerplexityBot"]INFOResolved effective root-path policy for crawler tokens blocked=0 allowed=21 unspecified=0

AI DiscoverabilityContent ReadinessEstablished

Content freshness signals

Content freshness signals is missing or incomplete.

50 Fail

Needs attention

Content freshness signals

Failed check
01

Issue

Content freshness signals are incomplete: missing any valid freshness date in Last-Modified, Schema.org, Open Graph, Dublin Core, generic meta, or sitemap lastmod.

Details

02

Why it matters

Freshness signals help agents, crawlers, and search systems decide whether content is current enough to cite, summarize, cache, or compare against newer sources.

Check name

Content freshness signals

Score

0/100

Status

fail

Category

Content Readiness

Maturity

Established

Goal

Expose modified and published dates for freshness-aware retrieval, citation, and ranking.

Result

Content freshness signals is missing or incomplete.

Validation steps

  1. Validate Last-Modified header

    No valid Last-Modified HTTP header was found.

    Last-Modified freshness signal is missing or invalid
  2. Validate structured freshness dates

    No Schema.org freshness dates were found.

    Structured freshness dates are missing or invalid
  3. Validate metadata freshness dates

    No Open Graph, Dublin Core, or generic meta freshness date was found.

    Metadata freshness dates are missing or invalid
  4. Corroborate with sitemap lastmod

    Add a sitemap entry with a valid lastmod for the canonical page URL when sitemap corroboration is useful.

    Sitemap lastmod corroboration is missing
Evidence log4 steps · 16 lines
Validate Last-Modified header [fail]! No valid Last-Modified HTTP header was found.INFOValidate Last-Modified headerINFOReading Last-Modified response headerFAILCheck Last-Modified header is present and parseable actual="missing" expected="valid" alternateDates=[]FAILLast-Modified header is missing or invalidValidate structured freshness dates [fail]! No Schema.org freshness dates were found.INFOValidate structured freshness datesINFOCollecting Schema.org freshness dates from JSON-LD, Microdata, and RDFaFAILCheck structured freshness date count actual=0 expected="> 0" invalidDateCount=0 formats={} alternateDates=[]FAILNo structured freshness dates foundValidate metadata freshness dates [fail]! No Open Graph, Dublin Core, or generic meta freshness date was found.INFOValidate metadata freshness datesINFOCollecting Open Graph, Dublin Core, and generic meta freshness datesFAILCheck metadata freshness date count actual=0 expected="> 0" metaDateCount=0 invalidDateCount=0 alternateDates=[]FAILNo metadata freshness dates foundCorroborate with sitemap lastmod [warning]INFOCorroborate with sitemap lastmodINFODiscovering sitemap candidates and matching the scanned page URL candidateCount=4 attempts=11WARNCheck matching sitemap lastmod actual="no match" expected="valid match when available" robotsStatusCode=200WARNNo matching sitemap lastmod found after sitemap attempts attempts=11

AI DiscoverabilityContent ReadinessEstablished

Author attribution

Author attribution is partially implemented.

48 Fail

Needs attention

Author attribution

Failed check
01

Issue

Applicable editorial content has no usable author, creator, publisher, visible byline, or HTML attribution signal.

Details

02

Why it matters

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

Check name

Author attribution

Score

5/100

Status

fail

Category

Content Readiness

Maturity

Established

Goal

Identify content authors or publishers for trust and attribution.

Result

Author attribution is partially implemented.

Validation steps

  1. Schema.org attribution

    Editorial content has no Schema.org author, creator, or publisher attribution.

    Schema.org author attribution is missing or incomplete
  2. Author identity quality

    No named author or publisher identity could be extracted.

    Author identity quality is incomplete
  3. Visible byline parity

    Structured author/publisher names were not found in visible byline text.

    Author attribution does not match visible byline content
  4. HTML attribution support

    No supporting HTML author metadata, rel=author link, or visible byline was found.

    HTML author attribution support is missing
Evidence log4 steps · 16 lines
Schema.org attribution [fail]! Editorial content has no Schema.org author, creator, or publisher attribution.INFOSchema.org attributionINFOChecking structured data for author, creator, and publisher contributorsFAILCheck named Schema.org author count actual=0 expected="> 0" authorCount=0 publisherCount=0 namedContributors=0 authors=[] publishers=[] formats=[]FAILNo Schema.org author, creator, or publisher attribution foundAuthor identity quality [fail]! No named author or publisher identity could be extracted.INFOAuthor identity qualityINFOChecking contributors for stable identity signalsFAILCheck identified contributor count actual=0 expected="> 0" namedContributors=0 identifiedContributors=[] unidentifiedContributors=[]FAILNo named contributor identity could be extractedVisible byline parity [fail]! Structured author/publisher names were not found in visible byline text.INFOVisible byline parityINFOComparing structured contributor names with visible byline evidenceFAILCheck visible attribution matches actual=0 expected="> 0 or visible byline present" visibleByline=false bylineCount=0FAILNo visible byline parity foundHTML attribution support [warning]! No supporting HTML author metadata, rel=author link, or visible byline was found.INFOHTML attribution supportINFOChecking meta author, rel=author, and visible byline supportWARNCheck HTML attribution support signals actual=0 expected="> 0" metaAuthor=false relAuthor=false visibleByline=falseWARNNo supporting HTML attribution signal found

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Score history

Public scan score over time

Public reports for this website origin. Select any point or report link to open that canonical report.

1 reports
Public score history report links
Scan dateScoreReadinessReport
60/100Level 3, Bot-AwareCurrent report