Full Answer

WooCommerce generates basic JSON-LD product schema automatically: name, description, price, currency, and a simple availability indicator. This covers the minimum viable structured data for traditional search engine rich results. But AI shopping agents evaluate product schema at a higher bar.

The missing fields fall into three categories. Trust signals: aggregate review ratings (aggregateRating with reviewCount and ratingValue), individual review markup, and brand as a structured field rather than embedded in the product name. These help AI agents assess product quality and merchant credibility without scraping unstructured page content.

Commercial attributes: shipping cost estimates (shippingDetails with shippingRate and deliveryTime), return policy details (hasMerchantReturnPolicy with returnPolicyCategory and merchantReturnDays), and product condition (itemCondition). AI agents making purchase recommendations need these to compare products across stores — a product without shipping data cannot be meaningfully compared to one that includes it.

Inventory intelligence: availability with granularity beyond simple in-stock or out-of-stock — including backorder status, preorder availability, and expected restock dates. Agents that guide purchasing decisions need to know not just whether a product is available now, but when it will be available if it is not.

Adding these fields requires either a structured data plugin that extends WooCommerce's default output, custom code in the theme's functions.php, or a server-side schema enrichment layer that injects complete markup before the page is served. The investment is modest — typically a few hours of configuration — but the visibility impact in AI-mediated shopping grows as more consumers interact with products through agents rather than traditional search results.