Most product catalogs were written for people who can fill in the gaps. A shopper who sees three photos, a short description, and a price can infer the material, guess the fit, and decide. An AI agent comparing your product against four others cannot infer anything. It reads the fields you provided, and whatever is missing simply counts against you.
This post is the practical version: what to fix, in what order, and how to tell whether it worked.
A missing specification is not a small gap. To a shopping agent, “material: not specified” and “material: wrong for this request” produce the same outcome, which is that you are not recommended.
Primo Collab
Start by auditing what is actually missing
Before improving anything, find out how bad it is. Export your product catalog and check what percentage of products have each field populated:
- Full title including the distinguishing attribute, not just a name
- Description longer than one sentence
- All relevant specifications for the category
- Every variant correctly named and in stock status
- At least one image per variant
- Weight and dimensions
- Category assignment
For most small stores this audit is uncomfortable. Products added in a hurry two years ago tend to be the ones missing the most, and they are often still selling.
Fix in this order
Not all fields carry the same weight. This ordering reflects what actually gets a product excluded from consideration.
1. Availability and price accuracy
If these are wrong, everything else is irrelevant, because the transaction fails and the failure is attributed to you. Real time accuracy matters more than richness here.
2. Specifications for the category
An agent filtering for “waterproof” needs a waterproof field, not a description that mentions rain. Category-specific attributes are what turn your product from unclassifiable into a candidate. Size, material, dimensions, compatibility, capacity, power requirements: whatever a buyer in your category would actually filter on.
3. Variant clarity
“Blue / Large” is fine. “B / L” is not. “Style 2” is worse. Variants with unclear names cannot be matched to a request and get skipped.
4. Descriptions that state facts
Marketing copy that describes a feeling gives an agent nothing to match against. You do not have to remove the personality, but the factual claims need to be present and explicit alongside it.
5. Shipping and returns as data
Cost, timeframe, destinations, return window, and who pays for returns. These are decision inputs. Buried in a linked policy page written in prose, they are effectively absent.
The structured data layer
Once the underlying data is right, structured markup is what exposes it. For a Shopify store this is mostly handled by the theme, but “mostly handled” hides a lot of broken implementations. Worth checking specifically:
| Field | Common problem |
|---|---|
| availability | Hardcoded as in stock regardless of actual inventory |
| price | Missing currency, or not updating with sale pricing |
| sku / gtin | Absent, which makes cross-merchant matching impossible |
| image | Only the primary image, not variant images |
| shippingDetails | Rarely implemented at all |
| hasMerchantReturnPolicy | Rarely implemented at all |
The last two are the ones almost nobody has, and they are increasingly the ones that differentiate a complete product record from a partial one.
How to tell whether it worked
This is the part most guidance skips. Three checks that give you real signal:
- Run your product URLs through a structured data validator. Errors and warnings here are concrete and fixable.
- Ask an AI assistant to find a product like yours and see whether you come up, and whether the details it reports are correct. Wrong details are more diagnostic than absence.
- Segment agent traffic in analytics and watch whether it grows and whether it converts.
The part that pays off either way
Everything above improves the store for human shoppers too. Complete specifications reduce pre-sale questions and returns. Accurate inventory prevents cancellations. Clear variant names reduce wrong-item orders. Proper structured data improves how your products appear in traditional search results.
If agentic commerce grows quickly, you are ready. If it grows slowly, you have a cleaner catalog, fewer support emails, and better search visibility. There is no version of this work that is wasted.
The mistake to avoid
Bulk-generating specifications to fill the gaps quickly. Wrong data is worse than missing data: missing data means you are not considered, wrong data means you are considered, chosen, and then the customer receives something that does not match. That damages returns, reviews, and the trust signals that agents will increasingly weigh.