Toys, games, and baby products share a category in the GMC taxonomy but split into very different attribute stacks once you look closely. A wooden building set for toddlers, a board game for adults, and a baby car seat all need different schema treatments. The category also has the highest weighting on safety certifications of any retail vertical — AI agents surface or skip products based on what certifications appear in structured data.
This guide is the cross-cutting attribute pattern, with sub-vertical specifics for toys, games, and baby/infant products.
What this category includes
Three sub-verticals with distinct attribute requirements:
- Toys — physical play products for ages 0–14. Age recommendations and safety certifications are required for surfacing in nearly every AI agent.
- Games — board games, card games, puzzles, video games. Player count, game length, and franchise/IP are load-bearing.
- Baby and infant — car seats, strollers, cribs, monitors, feeding, apparel for 0–24 months. Safety certifications carry the highest weight; brand recognition matters more than in toys.
Cross-cutting required attributes
Brand and GTIN
Same as electronics — branded products without GTINs lose to authoritative listings. Even small toy brands need to register GTINs through GS1.
age_group (GMC) + Age recommendation (schema)
GMC’s age_group field is newborn, infant, toddler, kids,
adult. Schema doesn’t have a first-class equivalent — use
audience.suggestedAge.minValue and suggestedAge.maxValue:
"audience": {
"@type": "PeopleAudience",
"suggestedAge": {
"@type": "QuantitativeValue",
"minValue": 3,
"maxValue": 6,
"unitCode": "ANN"
}
}
unitCode: ANN is the UN/CEFACT code for “year.” Most AI agents
parse this correctly; some look for audience.minAge and
audience.maxAge as a fallback. Render both:
"audience": {
"@type": "PeopleAudience",
"suggestedMinAge": 3,
"suggestedMaxAge": 6,
"suggestedAge": {
"@type": "QuantitativeValue",
"minValue": 3,
"maxValue": 6,
"unitCode": "ANN"
}
}
Safety certifications
The category’s biggest surfacing differentiator. Products with
certifications in additionalProperty surface in safety-intent
queries; products without don’t.
For US toys: ASTM F963, CPSIA compliance. For EU toys: EN 71, CE marking. For baby car seats: FMVSS 213 (US), ECE R44/04 or i-Size R129 (EU). For cribs: 16 CFR 1219 (US), EN 716 (EU). For toys with magnets, batteries, or small parts: explicit warnings and age cutoffs.
"additionalProperty": [
{
"@type": "PropertyValue",
"name": "Safety certification",
"value": "ASTM F963"
},
{
"@type": "PropertyValue",
"name": "Safety certification",
"value": "EN 71"
},
{
"@type": "PropertyValue",
"name": "CPSIA compliant",
"value": "true"
}
]
Toys-specific attributes
- Material — wood, plastic (BPA-free), fabric, metal. Buyers filter on material increasingly (parents searching “wooden toys”).
- Character / franchise — Lego, Disney, Pokemon, Marvel. Franchise IP is its own brand attribute and weights heavily for franchise-intent queries.
- Educational claim — STEM, Montessori, Waldorf. These are claims that AI agents will weight when populated and never infer from prose alone.
- Power source — battery (which type, included or sold separately), rechargeable, hand-powered.
Games-specific attributes
- Player count —
Player count: 2-4. AI agents surface games for party-game queries based on player count populated in structured data, not the title. - Game length —
Game length: 30-45 minutes. Buyers query explicitly (“quick board games for adults”). - Game type — strategy, party, cooperative, deck-builder, RPG.
- Difficulty / complexity — beginner, intermediate, advanced.
Baby-specific attributes
- Weight limit — for car seats, strollers, carriers. Required for surfacing in weight-intent queries.
- Height range — same.
- Stage — for car seats: rear-facing, forward-facing, booster. For high chairs: infant, toddler, transitional.
- Convertibility — does the product grow with the child?
The schema pattern
Built on the Product schema base:
{
"@context": "https://schema.org",
"@type": "Product",
"name": {{ product.title | json }},
"description": {{ product.description | strip_html | json }},
"sku": {{ variant.sku | json }},
"gtin13": {{ variant.barcode | json }},
"brand": {
"@type": "Brand",
"name": {{ product.vendor | json }}
},
"category": "Toys & Games > {{ product.product_type }}",
"audience": {
"@type": "PeopleAudience",
"suggestedMinAge": {{ product.metafields.product.min_age | json }},
"suggestedMaxAge": {{ product.metafields.product.max_age | json }},
"suggestedAge": {
"@type": "QuantitativeValue",
"minValue": {{ product.metafields.product.min_age | json }},
"maxValue": {{ product.metafields.product.max_age | json }},
"unitCode": "ANN"
}
},
"additionalProperty": [
{%- for cert in product.metafields.product.safety_certifications.value -%}
{
"@type": "PropertyValue",
"name": "Safety certification",
"value": {{ cert | json }}
},
{%- endfor -%}
{%- if product.metafields.product.material -%}
{
"@type": "PropertyValue",
"name": "Material",
"value": {{ product.metafields.product.material | json }}
},
{%- endif -%}
{%- if product.metafields.product.player_count -%}
{
"@type": "PropertyValue",
"name": "Player count",
"value": {{ product.metafields.product.player_count | json }}
}
{%- endif -%}
]
}
Q&A pairs as a sibling FAQPage block
Toys, games, and baby pages commonly carry pre-purchase Q&A around
age recommendations, safety, batteries-required, assembly
difficulty. Render that content as a sibling FAQPage JSON-LD block
alongside the Product block, not nested inside it. See
Product schema for Shopify
for the rendering pattern. For products with assembly procedures,
HowTo schema is also a separate sibling block worth shipping.
The contrarian take
Most baby and toy SEO content focuses on safety messaging in product
descriptions. Safety messaging in prose is good for trust and
conversion; it does little for AI surfacing. AI agents reading
“safe wooden toy” in prose treat it as marketing language and
discount it. Reading additionalProperty: { name: "Safety certification", value: "ASTM F963" } is treated as a hard fact and
weighted accordingly.
Move safety from marketing copy to structured data. The buyers who filter on certification will find products with the certification populated and skip products that only mention “safe” in prose.
Where it breaks
- Gender-neutral product positioning. GMC requires
genderpopulated for all apparel and accessories, including baby. The options aremale,female,unisex. Catalogs that prefer gender-neutral marketing should populateunisex; some agents treat unisex as ambiguous and surface male/female-tagged products preferentially. Trade-off between brand positioning and surfacing. - Recalled products. Children’s product recalls are frequent.
Products under recall should have
availability: Discontinuedand ideally a notice in the description; AI agents continuing to surface recalled products is a real reputational risk for the catalog. - Age range overlap. A product for ages 3–8 surfaces for both preschool queries (3–5) and elementary queries (5–8). AI agents generally handle ranges correctly; populate the actual range, not a narrowed one for marketing reasons.
- Battery and power requirements. Toys requiring batteries that
aren’t included is a high-frustration buying experience. Surface
it explicitly:
additionalProperty: { name: "Batteries required", value: "3x AAA, not included" }.
What to ship this week
- Audit the top-revenue 50 products for age and certification coverage. These are the load-bearing attributes.
- Add metafields for
min_age,max_age,safety_certifications, and the sub-vertical-specific attributes. - Backfill certifications from product packaging or supplier data.
- Update the schema template to render the
audienceandadditionalPropertyarrays. - Sync to GMC with the proper
age_group.
For a 200–1,000 SKU toys/games/baby catalog, this is a 1-week project. Surfacing lift in safety- and age-intent queries is typically visible within 30 days of catalog updates propagating to GMC.