Tonita

Get detailed information for one or more product listings. ## When to use this tool Call `tonita-get-listing-details` whenever you need to know more about specific products that have already appeared in search results. Common scenarios: - **Shopper asks about a product**: "Tell me more about that Nike shoe" — you need the full description, available sizes, and images. - **Preparing for visual search**: Before calling `tonita-search` with `query_images`, you need the `image_id` from this tool's response. - **Comparing products**: The shopper wants to compare two or three items side by side — fetch all their details in one call. - **Understanding before searching**: The shopper says "find me more like this". Call this tool first to understand what "this" is (its style, color, brand, price range), then construct a well-informed search. ## What you get back For each listing ID, the response includes: - **url**: The product page URL on the retailer's site. - **text / structured fields**: Title, description, price, brand, sizes, and other metadata (varies by retailer). - **thumbnail_image**: An object with `image_id` and `image_url` — the `image_id` is what you pass to `tonita-search` `query_images` for visual search. ## Examples ### Get details for a single product ```json {"listing_ids": ["thursdayboots.com!6c66be1f3d6c7f00"]} ``` ### Get details for multiple products to compare ```json {"listing_ids": [ "thursdayboots.com!6c66be1f3d6c7f00", "nike.com!a1b2c3d4e5f67890" ]} ``` ### Workflow: shopper says "more like this" about a listing 1. Call `tonita-get-listing-details` with `{"listing_ids": ["thursdayboots.com!6c66be1f3d6c7f00"]}` 2. Read the response: it's a "navy blue wool overcoat" with image_id "89e667d28336d70f" 3. Call `tonita-search` with `retrieval_queries=["navy blue wool overcoat"]`, `query_images=[{"image_id": "89e667d28336d70f"}]`, `prioritize_visual_similarity=true`

tonita-get-listing-details

Search Tonita's product catalog to find clothing and apparel items. ## When to use this tool Call `tonita-search` whenever a shopper wants to find, browse, discover, or compare products. Common scenarios: - **Direct product search**: "Find me red dresses under $100" - **Filtered browsing**: "Show me Nike running shoes, not from Amazon" - **Visual similarity**: "Find items that look like this jacket" (using a reference image from a previous `tonita-get-listing-details` call) - **Exploratory shopping**: "What summer dresses do you have?" ## How search works Search operates in two phases: 1. **Retrieval** — The engine fetches candidate products using `retrieval_queries`. Each query string is matched independently against the product catalog, and candidates are pooled together. More queries = broader candidate pool (up to a few variations). 2. **Ranking** — The pooled candidates are re-scored using `reranking_query` (falls back to the first `retrieval_queries` entry if omitted), combined with structured signals like product_type, gender, color, price, and brand preferences. When `prioritize_visual_similarity` is enabled and `query_images` are provided, the engine also retrieves visually similar products and visual-similarity scores influence the final ranking. ## Important rules - **One product type per call.** Searching for "shirts and pants and shoes" in one call produces poor results. Make three separate calls instead, each focused on one type. - **Keep queries objective and literal.** The search engine has no pop-culture or trend knowledge. "hot girl walk shoes" won't return good results — use "women's white athleisure sneakers" instead. - **Never reference other products in queries.** Don't write "similar to the Patagonia jacket". Instead, call `tonita-get-listing-details` first, read the product details, then construct a new descriptive query based on them. - **Use listing details for visual search.** When the shopper says "more like this" about a previously shown product, call `tonita-get-listing-details` to get the `image_id`, then pass it in `query_images` with `prioritize_visual_similarity` set to true. - **Omit what you don't know.** It is OK to leave optional fields as null. For example, if the user has no brand or color preference, leave those fields null — do not guess. - **Reuse `chat_id` within a conversation.** Pass the same optional `chat_id` on every related tool call in one shopping thread (`tonita-search`, `tonita-get-listing-details`) so exchanges can be correlated. Omit when the client has no session concept. - **Full details on search.** By default, `structuredContent.listingCards` stays lean (`listingId`, `title`, `currentPrice`). On ChatGPT/widget clients, richer cards (brand, URL, images) also arrive under result `_meta.listingCards` for the host UI — tools-only clients (generic MCP) do not include that `_meta` at all. Set `return_full_details=true` when the client LLM needs brand/URL/images in `structuredContent` itself (required for tools-only clients) — and **always pair that with a modest `max_results`** (e.g. 5–10). Do not pull full details for large carousels. ## Examples ### Simple search ```json { "retrieval_queries": ["red satin midi dress"], "product_type": "dress", "gender": "Womens" } ``` ### Search with brand and price filters ```json { "retrieval_queries": [ "running shoes for daily training", "men's cushioned running sneakers", "lightweight jogging shoes for men" ], "product_type": "running shoes", "gender": "Mens", "restrict_to_brands": ["Nike", "Adidas", "New Balance"], "price_constraints": "under $150" } ``` ### Search with color ```json { "retrieval_queries": ["leather crossbody bag"], "product_type": "bag", "gender": "Womens", "color": "black", "color_hex": "#000000", "price_constraints": "under $200" } ``` ### Visual search (find products that look like a reference listing) ```json { "retrieval_queries": ["minimalist white low-top sneaker"], "product_type": "sneakers", "gender": "Womens", "query_images": [{"image_id": "89e667d28336d70f"}], "prioritize_visual_similarity": true } ``` ### Visual search with a shopper photo (base64 or URL) ```json { "retrieval_queries": ["white leather low-top sneaker"], "product_type": "sneakers", "gender": "Womens", "query_images": [{"base64": "data:image/jpeg;base64,/9j/4AAQ..."}], "prioritize_visual_similarity": true } ``` ### Detailed search with custom reranking query ```json { "retrieval_queries": [ "summer floral wrap dress", "women's floral print midi dress" ], "reranking_query": "floral wrap dress; summer weight; midi length; feminine", "product_type": "dress", "gender": "Womens", "exclude_brands": ["SHEIN"], "price_constraints": "between $50 and $200", "carousel_title": "Summer floral wrap dresses" } ```

tonita-search