Pinch Grocery Prices (Australia)

MANDATORY for shopping lists. When the user gives you 2 or more grocery items, call this tool ONCE with the full list. Do NOT call decompose_product, search_products, or batch_search per item: this tool searches every item in parallel with built-in query analysis (category routing, brand aliases, subcategory boost) and returns a basket with per-item cheapest pick, per-retailer options, basket total, retailer totals, and a shareable share_url. If the user named the stores they shop at, pass them in 'retailers'; otherwise omit it to search all four. VALIDATION (mandatory before rendering): The search engine uses keyword matching, so wrong products leak through. Items flagged with a 'review' field on their cheapest pick are LIKELY WRONG and need your attention first. Then scan ALL picks (flagged or not) and check: (1) Is the product actually what was asked for? (e.g. 'frozen chips' is NOT 'frozen blueberries', 'sweet potato' is NOT 'potato', 'chicken fingers crumbed' is NOT 'chicken breast') (2) Is the size/form correct? (e.g. 400g pack is not a valid match for '2kg') (3) Is the selected option the cheapest CORRECT product, not just the cheapest product? If a pick is wrong: pick the correct product from by_retailer options for that item, or mark as '(check in store)'. Recalculate basket_total from your corrected picks. OUTPUT FORMAT: Line 1: '**Cheapest basket: $X.XX**' (use corrected basket_total). Then ONE markdown table with columns: Item | Product | Store | Price | Size. Then one line: 'View and share this basket: <share_url>'. Use '(check in store)' in the Product cell for items in items_missing or where no correct product exists. No preamble, no per-item narration, no follow-up offers.

ADVANCED / single item. Do NOT use this to build a shopping list: for any list of 2 or more items, call build_basket (one call, server-side). Use decompose_product only to break ONE item into structured search attributes when you intend to override build_basket's pick for that single item. This does not call any API; it is a structured reasoning step. OUTPUT: canonical_query, disqualifiers, and suggested_category for that single item. Fill in every field based on what a typical Australian family would mean by this item. CORE RULE: Unless the user literally specifies a brand, brand_preference MUST be null and the strategy is cheapest-first (unit_price_asc, no retailer filter).

ADVANCED / manual commit. Do NOT use this to build a basket from a shopping list: for any list of 2 or more items, call build_basket (it searches, picks the cheapest, totals, and returns a share_url in one call). Use save_basket only to commit a basket from product_ids you have ALREADY chosen yourself (for example, after overriding build_basket's pick on an item). It computes totals and returns a shareable basket URL. For each item, provide the query string and one product_id per retailer (the best match you found in batch_search results). You do NOT need a pick for every retailer; omit a retailer if no good match was found. IMPORTANT: Only call this after you have reviewed batch_search results and confirmed your picks. Do not pass product_ids blindly from search results.

ADVANCED / manual selection. Do NOT use this to build a shopping list: for any list of 2 or more items, call build_basket (one call, server-side, cheapest pick per item). Use batch_search only when you need the RAW candidate products for items you intend to select MANUALLY (for example, to override build_basket's pick on one item). It searches multiple items in parallel and returns raw candidate products per query (up to candidates_per_retailer per retailer) with NO filtering or scoring applied. WORKFLOW: 1. Call batch_search with your shopping list items 2. Review the candidates for each item. For each query, pick the single best product_id per retailer that actually matches what the user wants (correct product type, reasonable size, not a different product). If none of the candidates are a good match for a query, call batch_search again with a refined query (e.g., more specific terms, different wording) 3. Once you have confirmed product_id selections for all items, call save_basket with your picks TIPS: - Set product_type to help narrow results to the right category (e.g., fresh_produce for herbs and vegetables) - Each candidate includes a product_categories field (JSON array of retailer category labels like 'Yoghurt', 'Herbs & Spices', 'Sausages'). Use this to distinguish the actual product type when names are ambiguous (e.g., 'oregano' the herb vs 'oregano sausages') - Prefer products where the name clearly matches the query intent - For staples (milk, eggs, bread), the cheapest option is usually the store brand - If results look wrong (e.g., searching 'rosemary' returns snacks instead of herbs), retry with a more specific query like 'fresh rosemary' - Each item includes a 'cheapest' hint with one entry per retailer. This is the cheapest candidate whose product_categories match the inferred subcategory for the query, NOT necessarily the lowest unit_price overall: a clearly off-category cheaper item (e.g., a snack bar under 'peanut butter') is deliberately excluded. When no category signal is available it falls back to the raw lowest unit_price. Treat the hint as advisory and confirm against the full candidate list and the user's intended format. AMBIGUITY: When candidates for a query span clearly different product formats (e.g., microwave rice pouches vs dry rice bags, single-serve vs family/bulk sizes, flavoured vs plain variants, fresh vs frozen), ASK the user which format they prefer before selecting a product. Compare the size field across candidates to detect this: a spread from 125g to 5kg signals format ambiguity. Do not silently pick the cheapest option when it may be the wrong format (e.g., a 250g microwave pouch when the user likely wants a 1kg bag).

Compare prices for the same product across retailers using its barcode. Returns all retailers stocking the product, sorted cheapest first. About 7,800 products have barcode matches.

Find the cheapest option for a product across all Australian grocery retailers. Searches by name and returns results sorted by price ascending.

Get historical price data for a product. Shows price changes over time with stats (lowest, highest, average). History depth depends on your tier (30-365 days).

Get detailed information about a specific grocery product by its ID. Product IDs are returned in search results.

Get current grocery specials and promotions. Filter by retailer or category. Results include savings amounts. Updated weekly. Specials captured before the most recent Wednesday catalogue changeover (Coles/Woolworths) are excluded as unconfirmed; after a changeover the list can be near-empty until the next collection. Individual products still expose such prices via get_product with special_may_have_ended: true.

Search Australian grocery products across Woolworths, Coles, Aldi, and Harris Farm. Returns product name, price, brand, category, unit price, and whether it's on special.