A practical guide to JSON-LD Product schema for AI shopping agents

arnaud BUISINE — Fri, 15 May 2026 12:52:39 +0000

AI shopping is real now. ChatGPT, Perplexity, Gemini and Claude all recommend products in their answers. If you run an e-commerce store, you've probably wondered whether your products even show up.

Spoiler: probably not. After scanning ~500 stores at MerchantStamp, the average store scores 34/100 on AI-readiness. The most common gap, by far, is incomplete JSON-LD Product schema.

This is a practical guide to fixing that — written for developers shipping e-commerce sites who want to be visible to AI agents without becoming a structured-data expert.

Why AI agents need JSON-LD specifically

AI shopping agents don't crawl your site like Google does. They prefer two sources, in order:

Product feeds (Google Merchant, manufacturer feeds, marketplace APIs)
JSON-LD Product schema embedded on the page

If both exist and agree, your product is a candidate for recommendation. If they disagree, the agent down-weights the product (trust loss). If neither exists, you're invisible.

Schema.org Product is a W3C/Schema.org standard with ~50 properties. Most stores implement ~6 and stop. To actually be agent-readable, you need ~12.

The minimum viable Product schema

Here's what every product page should have:

\`html

{
"@context": "https://schema.org/",
"@type": "Product",
"name": "Waterproof Hiking Jacket",
"description": "Lightweight, breathable waterproof jacket. Sealed seams, packable.",
"image": "https://example.com/images/jacket-front.jpg",
"brand": { "@type": "Brand", "name": "OutdoorGear Co" },
"gtin13": "1234567890123",
"sku": "WRJ-BLUE-M",
"mpn": "OG-WRJ-2024",
"offers": {
"@type": "Offer",
"url": "https://example.com/products/waterproof-jacket",
"priceCurrency": "EUR",
"price": "149.99",
"availability": "https://schema.org/InStock",
"seller": { "@type": "Organization", "name": "Example Store" }
},
"aggregateRating": {
"@type": "AggregateRating",
"ratingValue": "4.7",
"reviewCount": "328"
}
}

`\

The five properties everyone forgets

In rank order of impact on AI recommendation rate:

gtin13 / gtin12 / gtin — without it, AI can't cross-reference your product. Most underrated field by a country mile.
availability — must match the visible page state. "InStock" on a sold-out product is a trust killer.
aggregateRating — agents heavily weight this. Even modest ratings (4.0+) significantly improve recommendation rate.
mpn — for products with a manufacturer part number, include it. Adds redundancy.
shippingDetails inside offers — agents check this to filter by user location.

How to verify it works

Three tools to validate before shipping:

Google Rich Results Test — https://search.google.com/test/rich-results
Schema.org Validator — https://validator.schema.org
MerchantStamp audit — https://merchantstamp.com/scan (free, no signup, runs the full 23-check battery agents actually use)

The MerchantStamp tool goes further than the others because it checks not just schema validity, but agent-specific requirements: which bots are allowed in robots.txt, whether your policies are machine-readable, whether your feed and JSON-LD agree on price, etc. Full methodology at https://merchantstamp.com/methodology.

The bigger picture

Structured data is no longer a "rich snippets in Google" thing. It's the substrate AI shopping agents are built on. ChatGPT alone has 200M+ weekly users and shopping is becoming a flagship feature.

If you're shipping e-commerce code in 2026, complete JSON-LD Product schema is no longer optional. It's the new equivalent of having a sitemap.xml.