Cheapest Grocery Basket

Price a whole shopping list at each store

price_basket

Price an entire shopping list at every collected store in a ZIP, one complete basket per store. Returns per-store line items with quantities and line totals, the basket subtotal, exactly which requested items that store is missing, whether the basket is complete, stock flags, and the oldest price in the basket so staleness is visible. Use this when the shopper will make one trip and wants to know where the whole list is cheapest. Accepts quantities as 'milk x2'. Costs $0.01 USDC per call via x402 on Base.

Input Schema

TableJSON Schema

Name	Required	Description
`zip`	Yes	5-digit US ZIP code, e.g. '30501'.
`mode`	No	Optional fulfillment intent: 'pickup' or 'delivery'. Recorded on the response; delivery fees and minimums are not collected yet and are returned as null rather than estimated.
`items`	Yes	Comma-separated shopping list, optionally with quantities, e.g. 'milk x2, eggs, bread, butter'. A JSON array of {name, quantity} is also accepted.
`stores`	No	Optional comma-separated chains to restrict to, e.g. 'aldi,publix'.
`maxAgeDays`	No	Optional freshness limit in days (default 14, max 120). A price read longer ago than this is withheld rather than returned as if current; raise it to accept older readings.

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description carries the full disclosure burden. It covers the returned fields, flags staleness via the oldest price, and discloses the $0.01 USDC cost via x402. It stops short of explicitly stating non-mutating behavior or side effects, but it is substantially transparent.

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?

Four sentences, each earning its place: core action, return contents, usage context, and cost/quantity format. It is front-loaded and compact without padding.

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

Completeness4/5

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

Despite having no output schema and no annotations, the description supplies the key output fields and use context needed to call the tool correctly. It could be slightly more complete by hinting at response structure for missing stores or empty results, but the schema covers parameters and the description covers behavior.

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

Parameters3/5

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

The input schema already describes all five parameters with 100% coverage, so the description is not required to add much. It reinforces the accepted quantity syntax ('milk x2') but adds no deeper semantic detail beyond what the schema provides, matching the baseline of 3.

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 a specific verb and resource: 'Price an entire shopping list at every collected store in a ZIP, one complete basket per store.' It also enumerates concrete outputs (subtotal, missing items, completeness, stock flags), making the purpose unmistakable and distinct from siblings like demand_report or find_product.

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

Usage Guidelines4/5

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

It explicitly states when to use the tool: 'Use this when the shopper will make one trip and wants to know where the whole list is cheapest.' It does not name sibling alternatives or state when-not-to-use it, so it misses the full 5-level guidance but provides clear context.

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

All Cheapest Grocery Basket tools Inspect

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TDQS

A4.1/5.0

Disambiguation4/5

find_product, demand_report, and request_data are clearly distinct, but price_basket and cheapest_basket both operate on a whole shopping list. Their descriptions define different output styles (itemized per-store baskets vs. optimized buying decision), so an agent can usually choose correctly, though some overlap remains.

Naming Consistency4/5

All names are lowercase snake_case and descriptive, but find_product, price_basket, and request_data follow a verb_noun form while cheapest_basket and demand_report are noun phrases. This is a minor inconsistency rather than a chaotic naming scheme.

Tool Count4/5

The four listed tools are well-scoped for grocery price/basket queries plus data demand. However, the header declares five tools while only four are described, so the exact intended surface is slightly uncertain.

Completeness4/5

Core grocery use cases are covered: single-item lookup, one-trip basket pricing, multi-store optimization, and data demand. A minor gap is the lack of dedicated tooling for broader store or product meta-information beyond the price and stock fields already returned.

Resources

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