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Pinch Grocery Prices (Australia)

batch_search

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).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesList of grocery items to search (1-20 items).
retailersNoFilter to these retailers only. If omitted, all 4 are searched.
candidates_per_retailerNoNumber of candidate products to return per retailer per item (1-30, default 10). Use more candidates if you expect ambiguous results.

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully discloses behavior: searches in parallel, returns raw unranked candidates, includes product_categories for disambiguation, explains the 'cheapest' hint semantics including its fallback logic, and warns about format ambiguity requiring user confirmation. This goes far beyond a generic tool description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with clear sections (workflow, tips, ambiguity). Every sentence serves a purpose — from the upfront warning against misuse to detailed field semantics. No fluff; the length is justified by the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description fully covers the tool's behavior, parameters, return candidate characteristics, and edge cases like ambiguous formats. Combined with the input schema, an agent has enough context to use the tool correctly and handle subtle decision points.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds substantial meaning: explains how product_type narrows results and gives concrete examples per enum, notes category overrides product_type, and advises raising candidates_per_retailer for ambiguous queries. It enriches the schema's basic definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'ADVANCED / manual selection' and clearly states it searches multiple items in parallel and returns raw candidate products with NO filtering or scoring. It explicitly distinguishes from build_basket and save_basket, making the tool's purpose unambiguous and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use and when-not-to-use guidance: 'Do NOT use this to build a shopping list' and directs users to build_basket instead. Includes a step-by-step workflow and tips for refining queries, making appropriate usage highly actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have distinct roles with explicit workflow guidance, but search_products and get_cheapest overlap in purpose (both search by name and return price data), and batch_search vs search_products could be confused without careful reading. The detailed descriptions mitigate this but don't eliminate it.

Naming Consistency4/5

Tool names are consistently snake_case, mostly verb_noun or get_noun. batch_search is a minor deviation (noun+verb) but still readable and consistent with the style.

Tool Count5/5

10 tools is well-scoped for a grocery price comparison server, covering search, details, history, specials, and basket management without excessive redundancy.

Completeness4/5

The server covers the core domain well: product search, price comparison, history, specials, and basket building/saving. Minor gaps include no way to fetch a saved basket programmatically and no explicit basket editing, but these can be worked around.

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