The Brick Yard
brickyardsf.com is largely invisible to agents.
See what an AI agent reads on this site →
13 of 65 points available
agent-readiness level, Not Ready
things to fix
Provisional: the action test and the assistant citation check still to come; the hatched part of the bar is not measured yet.
What to fix first
- No structured data for the business +15 points
Add schema.org JSON-LD for the business (CafeOrCoffeeShop, BarOrPub or the closest type, with name, address, telephone and openingHoursSpecification, plus servesCuisine). It is the one block every assistant reads first; the fix kit below has it filled in. How to fix it
- No booking or ordering path at all +15 points
Agents have nothing to act on. Add a booking or ordering page on your own domain. How to fix it
- robots.txt blocks GPTBot, ClaudeBot, PerplexityBot, Google-Extended, OAI-SearchBot, ChatGPT-User, Claude-User, Perplexity-User +10 points
These are the crawlers behind ChatGPT, Claude, Perplexity and Google's AI features. Remove the Disallow lines for them, or add explicit Allow rules. How to fix it
- No menu or price list found +7 points
Agents answer 'what do they offer' from marketplaces instead. Add a menu or price list page and link it from the homepage. How to fix it
- Almost no text without JavaScript +2 points
Crawlers that do not run scripts see an empty page. Render the menu, hours and address as plain HTML. How to fix it
- No llms.txt no points, still worth doing
Optional today, cheap to add: a short text file describing the business for language models. How to fix it
No one is listed yet to fix this in San Francisco cafes and bars. List your agency or yourself: free, reviewed by hand.
Already right
- AI crawlers get the page GPTBot, ClaudeBot and PerplexityBot all received a normal response.
Builder: unknown · Front: cloudflare
What to paste
Filled from the business's own record and this scan. Check every line before it goes live; where it goes on your builder is in the guides.
Structured data (JSON-LD)
One block in the page's head, or in the builder's custom-code slot. Replace anything that is wrong. Guide.
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "BarOrPub",
"name": "The Brick Yard",
"url": "https://brickyardsf.com/",
"telephone": "+1 415 4004712",
"email": "info@brickyardsf.com",
"address": {
"@type": "PostalAddress",
"streetAddress": "Union Street 1787",
"postalCode": "94123",
"addressLocality": "San Francisco"
},
"geo": {
"@type": "GeoCoordinates",
"latitude": 37.7977877,
"longitude": -122.4286121
}
}
</script>
robots.txt
Add these lines to /robots.txt and delete any Disallow rule that names these crawlers. Guide.
# Let AI assistants read the site: they send people to you; a marketplace keeps them. # Remove any Disallow lines for these names elsewhere in this file. User-agent: GPTBot Allow: / User-agent: ClaudeBot Allow: / User-agent: PerplexityBot Allow: / User-agent: Google-Extended Allow: / User-agent: OAI-SearchBot Allow: / User-agent: ChatGPT-User Allow: / User-agent: Claude-User Allow: / User-agent: Perplexity-User Allow: /
llms.txt
Save as /llms.txt at the root of the site. Guide.
# The Brick Yard > bar or pub in Union Street 1787 94123 San Francisco, San Francisco cafes and bars. Phone +1 415 4004712. ## About - Website: https://brickyardsf.com/ - This file follows llms.txt (https://llmstxt.org): a short plain-text description for language models.
380 of 516 in San Francisco cafes and bars
Top 74% of the sites scanned in this index. Just above: Bistro Gambrinus F, Verdi Club F. Just below: Tope F, Little Window by Bicycle Banh Mi F.
| What the top tenth of the index does | Top 10% | This site |
|---|---|---|
| Take bookings or orders on their own domain | 98% | no |
| Publish the menu or price list as HTML | 81% | no |
| Let AI crawlers in (no edge block, no robots.txt block) | 100% | no |
| Structured data with address, phone and hours | 23% | no |
| Have an llms.txt | 13% | no |
The top tenth is the 52 best-scoring reachable sites of the index at the last refresh.
Nearest places in the index
| Place | Distance | Grade | Direct booking or ordering |
|---|---|---|---|
| Bus Stop | 185 m | F | no |
| Left Door | 189 m | A | yes |
| Müesi Cafe | 391 m | F | no |
| Kopiku indonesian | 455 m | D | no |
| Tonic | 574 m | F | no |
| Loving Cup | 574 m | F | no |
| Saint Frank coffee_shop | 581 m | D | no |
| Teaspoon bubble_tea | 603 m | B | yes |
| Sidewalk Café sandwich | 664 m | C | yes |
| Balboa Cafe | 667 m | C | yes |
Level 0 of 5, Not Ready
The standards an agent looks for before it reads a page, on the same criteria as Cloudflare's Agent Readiness scan. Not part of the score; each missing item links to a how-to. Cloudflare's report.
| Link headers on the homepage | no how |
| sitemap.xml | no how |
| AI bot rules in robots.txt | yes |
| Content Signals in robots.txt | no how |
| Markdown on Accept: text/markdown | no how |
| llms.txt | no how |
| API catalog (RFC 9727) | no how |
| MCP server card | no how |
| A2A agent card | no how |
| Agent Skills index | no how |
| OAuth or OIDC discovery | no how |
| OAuth protected resource metadata | no how |
| auth.md | no how |
| Web Bot Auth key directory | no how |
| ARD capability manifest | no how |
| DNS-AID records | no how |
| WebMCP tools on the page | no how |
| JSON-LD for the business | no |
What assistants answer
Each assistant is asked three questions about this business: the official website, where to book, where to order. The cell says where the answer sent people.
Not asked yet. Assistants are asked about the top-ranked businesses in each city index, and about every claimed business the moment it is claimed; claim this page to be included.
Show the score
Owners who score well embed the badge; it links back here and updates with every scan.
<a href="https://directindex.org/r/brickyardsf.com"><img src="https://directindex.org/badge/brickyardsf.com.svg" alt="Agent-Direct Score F" width="236" height="40"></a>
Own this business?
Fixed something, or think a check is wrong? Rescans are free. Claiming takes one email to confirm; after that the three assistants are asked about the business right away, the site is rescanned weekly, and you get a message only when the score changes.
Deep report Ten questions the way people ask, each assistant's full answer, the eight nearest similar places, the fix plan and a PDF: 19 EUR, one-off.