{
  "id": "G05",
  "url": "https://cartforagents.com/guides/negative-product-attributes",
  "title": "Negative product attributes: publish who and what your product is not for",
  "description": "Represent not-for statements and fit exclusions in a proposed fact schema with merchant examples, not instructions that override a shopper.",
  "answer": "Publish each real exclusion, incompatibility and use limit as a plain fact about the product: what it is not for, the boundary with its unit, which variants it applies to and where the fact comes from. Put it in visible text near the top of the page and in a structured field, never as an instruction aimed at an AI assistant. Only call something incompatible when you know it is; if you have not tested it, say that instead.",
  "take": "A true \"not for\" line is one of the most underused facts in ecommerce. It turns away the wrong buyer, saves a return, and gives an assistant something real to filter on.",
  "summary": "A true \"not for\" line sends the wrong buyer elsewhere and gives an assistant something real to filter on. Write it as a fact about the product, never as an instruction to the assistant.",
  "claims": [
    {
      "text": "OpenAI says ChatGPT considers the shopper's query and context when choosing products, and gives the example that if a shopper has said they dislike clowns, it may leave out clown costumes. It lists structured metadata such as price and product description among the inputs, and recommends that shoppers verify products meet their specific needs before buying.",
      "source_title": "OpenAI Help Center: Shopping with ChatGPT Search, How product results are selected",
      "source_url": "https://help.openai.com/en/articles/11128490-shopping-with-chatgpt-search",
      "source_date": "2026-10-27",
      "method": "documented",
      "limitation": "Living help article checked on this date. It describes inputs, not weights, and does not say how merchant-written limits are used."
    },
    {
      "text": "In a supplied-snippet simulation, adding \"Not for rooms over 500 sq ft\" to an invented humidifier's spec line changed held-out picks from 134 to 118 of 180 on Claude (three models pooled), 20 to 20 of 60 on GPT-6.1-Sol, 19 of 59 to 17 of 57 on Gemini 3.8 Flash and 15 of 55 to 17 of 57 on Gemini 3.1 Pro. For an invented lip gloss, the version with \"Not for opaque colour coverage\" was picked 168 of 180 times on Claude (same as the factual version), 59 of 60 on GPT-6.1-Sol (against 60), 45 of 60 on Gemini 3.1 Pro (against 53) and 32 of 60 on Gemini 3.8 Flash (against 49).",
      "source_title": "Cart for Agents: Vague vs specific product facts, simulated picks by Claude, GPT and Gemini models",
      "source_url": "https://cartforagents.com/research/vague-specific-product-facts-simulation",
      "source_date": "2026-10-27",
      "method": "controlled-simulation",
      "limitation": "Invented products among real listings we supplied; models never searched or opened pages. No shopper brief needed a large room or opaque colour, and there was no \"none of these\" option, so the test cannot show whether a limit helps an agent rule a product out. For gloss, the limit version also changed format (prose to spec line). Not live ChatGPT or Gemini results."
    },
    {
      "text": "Schema.org defines negativeNotes for negative considerations, typically in pro/con lists for reviews. It says the property can also be used on a Product, though because product descriptions tend to emphasise positive claims, that use may be relatively unusual.",
      "source_title": "Schema.org: negativeNotes",
      "source_url": "https://schema.org/negativeNotes",
      "source_date": "2026-10-27",
      "method": "documented",
      "limitation": "A vocabulary definition, dated by our check. We found no documentation saying any search engine or shopping assistant reads negativeNotes on a product."
    },
    {
      "text": "Schema.org's Product type has positive compatibility pointers: isAccessoryOrSparePartFor and isConsumableFor, each pointing to the product it works with.",
      "source_title": "Schema.org: Product",
      "source_url": "https://schema.org/Product",
      "source_date": "2026-10-27",
      "method": "documented",
      "limitation": "Vocabulary only; there is no matching \"not compatible with\" property, and we do not know which consumers read these."
    },
    {
      "text": "OpenAI's product feed reference says a final-sale item is sent with accepts_returns set to false, that an empty value does not mean zero or false, and that warning and age-restriction fields do not establish purchase restrictions.",
      "source_title": "OpenAI Commerce: Products (file upload feed reference), Value conventions, Returns and Compatibility notes",
      "source_url": "https://developers.openai.com/commerce/specs/file-upload/products",
      "source_date": "2026-10-27",
      "method": "documented",
      "limitation": "Direct-feed format guidance checked on this date. Shopify merchants are integrated through Shopify Catalog, and the reference does not say how ChatGPT uses these fields in ranking."
    },
    {
      "text": "Shopify says Catalog Mapping can source product data stored in custom fields such as metafields and metaobjects for agentic storefronts.",
      "source_title": "Shopify Help Center: Shopify Catalog and product discovery for agentic storefronts",
      "source_url": "https://help.shopify.com/en/manual/online-sales-channels/agentic-storefronts/products",
      "source_date": "2026-10-27",
      "method": "documented",
      "limitation": "Living help page checked on this date. It does not promise that a mapped field reaches every assistant unchanged."
    },
    {
      "text": "We hand-picked 15 listings from Shopify's agent catalog (October 26, 2026) whose description stated a clear limit on fit, compatibility or use. Shopify's generated metadata for the same records kept an explicit negative statement in 1 (and only one of that listing's two limits), restated the limit only as a positive fit range in 5, did not carry it in 8, and was absent for 1. None of the 15 titles stated the limit.",
      "source_title": "Cart for Agents: How we checked (negative product facts guide)",
      "source_url": "https://cartforagents.com/guides/negative-product-attributes#how-we-checked",
      "source_date": "2026-10-26",
      "method": "observed",
      "limitation": "A hand-picked set from one snapshot, chosen because the description stated a limit; it is not a rate for the catalog. We cannot see how the generated fields are made, and we did not check any limit against the product."
    },
    {
      "text": "In the snapshot for \"toddler snowsuit rated for minus 30\", one description rated the insulation in the hood to -30°C. The generated specification list in the same record said the insulation was rated to -30°C, without the word hood.",
      "source_title": "Cart for Agents: Receipts cited by our guides (Shopify agent-catalog snapshot, Oct 26, 2026)",
      "source_url": "https://cartforagents.com/research/agent-shopping-research#receipts-cited-by-our-guides",
      "source_date": "2026-10-26",
      "method": "observed",
      "limitation": "One record. The full garment rating is unknown to us; we did not inspect the product or the manufacturer documentation."
    },
    {
      "text": "An auto-parts merchant on Reddit described ChatGPT getting a product's fitment wrong for a customer: \"fitment is a key thing and ChatGPT got it wrong, initially the customer blamed us\".",
      "source_title": "Reddit r/shopify: reply in \"Orders from ChatGPT\"",
      "source_url": "https://www.reddit.com/r/shopify/comments/1uly7ub/comment/oviealt/",
      "source_date": "2026-07-04",
      "method": "observed",
      "limitation": "One merchant's account, unverified. It does not show what the listing said or why the assistant got the fit wrong."
    },
    {
      "text": "The authors of SafeGEO report that rewriting seller-controlled sources can make flawed products more likely to enter AI recommendation sets: on average, the rate rose by up to 83.2 percentage points across their 22 attack variants and 600 recommendation cases (for example, from 12.7% to 95.9% on one model). Simple agent-side defenses reduced harmful promotion by up to 39.2 percentage points without restoring the no-rewrite performance.",
      "source_title": "SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents (arXiv v2)",
      "source_url": "https://arxiv.org/abs/2606.28356",
      "source_date": "2026-09-04",
      "method": "controlled-simulation",
      "limitation": "Author-reported benchmark with constructed cases and controlled rewrites; it does not measure deployed shopping assistants or how common such rewriting is."
    }
  ],
  "sources": [
    {
      "title": "OpenAI Help Center: Shopping with ChatGPT Search",
      "url": "https://help.openai.com/en/articles/11128490-shopping-with-chatgpt-search",
      "accessed": "2026-10-27"
    },
    {
      "title": "Cart for Agents: Vague vs specific product facts, simulated picks by Claude, GPT and Gemini models",
      "url": "https://cartforagents.com/research/vague-specific-product-facts-simulation",
      "accessed": "2026-10-27"
    },
    {
      "title": "Schema.org: negativeNotes",
      "url": "https://schema.org/negativeNotes",
      "accessed": "2026-10-27"
    },
    {
      "title": "Schema.org: Product",
      "url": "https://schema.org/Product",
      "accessed": "2026-10-27"
    },
    {
      "title": "OpenAI Commerce: Products (file upload feed reference)",
      "url": "https://developers.openai.com/commerce/specs/file-upload/products",
      "accessed": "2026-10-27"
    },
    {
      "title": "Shopify Help Center: Shopify Catalog and product discovery for agentic storefronts",
      "url": "https://help.shopify.com/en/manual/online-sales-channels/agentic-storefronts/products",
      "accessed": "2026-10-27"
    },
    {
      "title": "Cart for Agents: Receipts cited by our guides",
      "url": "https://cartforagents.com/research/agent-shopping-research#receipts-cited-by-our-guides",
      "accessed": "2026-10-27"
    },
    {
      "title": "Reddit r/shopify: reply in \"Orders from ChatGPT\"",
      "url": "https://www.reddit.com/r/shopify/comments/1uly7ub/comment/oviealt/",
      "accessed": "2026-10-27"
    },
    {
      "title": "SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents (arXiv v2, 2026-09-04)",
      "url": "https://arxiv.org/abs/2606.28356",
      "accessed": "2026-10-27"
    }
  ],
  "limitations": [
    "The schema on this page is a proposal for organizing your own product data. No platform requires it, and we have not tested whether using it changes how any assistant ranks or recommends a product.",
    "Our simulation never gave a shopper a need the product could not meet, and never offered a \"none of these\" answer. It cannot show whether a stated limit helps an agent turn the wrong buyer away, and it should not be read as evidence that limits cost picks.",
    "The 15 catalog listings were hand-picked because they stated a limit. They come from one unpersonalized Shopify agent-catalog snapshot (October 26, 2026) and show what the records carried, not what any assistant showed a shopper.",
    "We did not verify any of the limits quoted here against the products. They are examples of how merchants wrote limits, not product advice.",
    "Store and brand names are withheld, and quoted excerpts are kept short."
  ],
  "related": [
    "https://cartforagents.com/guides/agent-commerce-optimization",
    "https://cartforagents.com/guides/product-claim-provenance",
    "https://cartforagents.com/guides/product-constraint-coverage",
    "https://cartforagents.com/guides/ai-product-descriptions",
    "https://cartforagents.com/guides/product-variant-structured-data",
    "https://cartforagents.com/research/vague-specific-product-facts-simulation"
  ],
  "published": "2026-10-27",
  "updated": "2026-10-27",
  "checked": "2026-10-27",
  "kind": "guide"
}