- Visibility39
- Citations3
- Sentiment83
- Site quality80
- AI readiness21
AI assistants name 1stDibs in 40% of their answers, much more often than the typical company in retail at 6.7%. Its overall score is 45 out of 100, ranking 102nd of 2,047 companies in the study. The company’s website passes 3 out of 14 technical checks for how ready it is for AI tools.
We asked 15 unbranded questions about the retail market: none named 1stDibs. AI assistants named it in 24 of 60 answers (40.0%). The typical retail company is named in 6.7% of answers. By how often it is named, 1stDibs ranks 49 of 640 in its sector.
Every dot is a company in the study. Hover one to see it; click to open its report.
This companySame sectorEvery other companyNever named
Five parts, each scored out of 100.
How often AI assistants name the company
How often AI uses the company's own website as a source
How positively AI describes the company
How well built the website's own pages are
How easily AI tools can read and use the website
Visibility is calculated from the answers on this page. How the score is built.
640 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“How do luxury vintage marketplaces differ from general online auction sites?”
Luxury vintage marketplaces differ from general online auction sites mainly in curation, authentication, buyer quality, and pricing structure. Luxury vintage platforms are typically more selective and often focus on high-value categories like designer fashion, watches, handbags, jewelry, and fine art, while general auction sites like eBay cast a much wider net across almost every product category.
Luxury vintage marketplaces tend to be curated and may vet both sellers and items, which helps attract buyers who are looking for provenance, condition reports, and authentici …
The AI’s answer as we received it, shortened and not checked for accuracy.
How the mentions in these answers were split between this company and its rivals
Answers naming it, out of answers received
What this website publishes for an AI agent to read, use and buy from, on a scale of six levels.
- 0
- 1
- 2
- 3
- 4
- 5
Level 0: Not yet agent-ready. Agents struggle to find basic information about the site.
- 2 of robots.txt, Sitemap, Link headers
Not by itself. 1stDibs publishes none of the purchase protocols we look for. An agent would have to click through the checkout like a person.
- UCPNot present
- ACPNot present
- AP2Not present
- x402Not present
- MPPNot present
Show every check, for your technical teamHide the checks
- study: 92.0%robots.txtPassed
A valid robots.txt with crawl rules at the site root.
- study: 86.8%SitemapNot met
Serve an XML sitemap and reference it from robots.txt.
- study: 0.3%Link headersNot met
Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.
- study: 5.5%DNS for AI DiscoveryPassed
SVCB or HTTPS records under _agents that advertise agent endpoints.
- study: 1.9%Markdown negotiationNot met
Return a markdown version when asked with Accept: text/markdown, for example at the CDN.
- study: 25.1%llms.txtNot met
Publish an llms.txt with a short summary and the key links.
- study: 93.4%AI bot rulesPassed
Explicit robots.txt rules for AI crawlers such as GPTBot, ClaudeBot and PerplexityBot.
- study: 1.4%Content SignalsNot met
Add a Content-Signal line for search, ai-input and ai-train to robots.txt.
- study: 0.2%Web Bot AuthNot present
Only relevant if you run your own agents or crawlers: publish a signing key directory.
- study: 0.3%API CatalogNot met
List public APIs in a linkset at /.well-known/api-catalog.
- study: 11.8%OAuth discoveryNot met
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourceNot met
Serve protected resource metadata at /.well-known/oauth-protected-resource.
- study: 0.0%auth.mdNot met
Publish an auth.md describing how agents sign in.
- study: 0.2%MCP Server CardNot met
Offer an MCP server and publish its server card under /.well-known.
- study: 0.1%A2A Agent CardNot met
Publish an agent card at /.well-known/agent-card.json.
- study: 0.2%Agent SkillsNot met
Publish a skills index with the main tasks agents can do.
- study: 33.7%WebMCPCould not check
Register key actions such as search or cart as WebMCP tools.
- study: 0.1%ARD manifestNot met
Publish an ai-catalog.json listing every agent interface.
Score 26 of 100 · 97% of checks could run · checked on October 5, 2026 · www.1stdibs.com · 2,047 companies scanned
3 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.
These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.
Show the 14 checksHide them
- Passedrobots.txt publishedstudy: 85.0%
- Not metXML sitemapstudy: 73.2%
- Not metHTTP Link headers (RFC 8288)study: 29.0%
- Not metMarkdown content negotiationstudy: 8.3%
- Not metllms.txt publishedstudy: 21.7%
- PassedToken budget (page weight)study: 95.2%
- PassedExplicit AI bot rulesstudy: 13.0%
- Not metAPI Catalog (.well-known/api-catalog)study: 0.2%
- Not metOAuth Authorization Server discoverystudy: 10.1%
- Not metOAuth Protected Resource discoverystudy: 9.3%
- Not metMCP Server Cardstudy: 0.0%
- Not metA2A Agent Cardstudy: 0.0%
- Not metWebMCP toolsstudy: 4.2%
- Not metAgent Skills declaredstudy: 0.0%
This website sets rules for 2 AI crawlers by name. All others follow its general rules.
- GPTBotpartly
- ClaudeBot*allowed
- Anthropic-AIpartly
- Google-Extended*partly
- Applebot-Extendedpartly
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespiderpartly
- CCBotpartly
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBotpartly
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-Userpartly
- Claude-Userpartly
- Perplexity-Userpartly
- DuckAssistBotpartly
- MistralAI-Userpartly
Read from the website’s own rules file (robots.txt). An asterisk marks a crawler the file names. The rest follow its general rules. “Partly” means only some pages are blocked.
How many times each website was listed as a source in these answers
In the study as US retailers: listed as a retailer in Wikidata, a public database, and among the websites Cloudflare Radar ranks as visited.
- How do luxury vintage marketplaces differ from general online auction sites?
- Typical price range for curated vintage designer accessories online
- Where can I find authentic antique furniture online for collectors in the US?
- Are high-end vintage fashion marketplaces worth the premium prices?
- What to do if an item’s condition doesn’t match the listing on a vintage marketplace?
- How to verify authenticity when purchasing luxury antiques online
- Tips for sourcing unique pieces for luxury home décor online
- How to discover unique mid-century modern pieces online
- Antique furniture vs. vintage furniture: what’s the key difference for collectors?
- Alternatives to online luxury vintage marketplaces for sourcing antiques
- Best places to shop for high-end vintage jewelry from trusted sellers
- Is shopping for fine art online safe for collectors?
- Benefits of buying art from a curated marketplace versus individual dealers
- How to handle shipping issues with antique furniture bought online
- How collectors use online platforms to find rare statement jewelry
Columns, left to right: OpenAI, Claude, Perplexity, Gemini. The AI assistants often disagree. Across the study, on 40% of the questions where a company is named, only one of the four names it.
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<a href="https://aeo-audit.rezolve.com/us/1stdibs.com"><img src="https://aeo-audit.rezolve.com/us/badges/1stdibs.com.svg" alt="Overall score 45 of 100, US AI Visibility Study 2026" width="280" height="64"></a>