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Price Drop Back: A store's price adjustment and price match policy

pricedropback_get_store_policy
Read-onlyIdempotent

Price Drop Back. A store's price adjustment and price match policy. For requests like "does Target do price matching?", "does John Lewis refund if the price drops?", "Currys price promise", "what's Best Buy's price adjustment policy?", "will Costco refund the difference?". With an item name it adds links to check today's price.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemNoOptional item name, up to 100 characters, to link today's prices for it.
storeYesStore name in everyday words, up to 60 characters.
countryNoTwo-letter country code. GB (or UK) gives the UK policy where the store has one (Apple, Amazon, IKEA) and UK rules; it also picks the price links (US, GB and IE get local Amazon and eBay links). Default from the request.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is supplied elsewhere. The description adds one genuinely new behavioral trait -- that supplying an item name changes the output to append today's-price links -- but says nothing about response size, freshness, or coverage limits.

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

Conciseness4/5

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

Purpose is front-loaded, then examples, then the item-name behavior. The redundant repetition of "Price Drop Back" as the first fragment is minor waste, and the five examples are slightly long but each targets a distinct phrasing, so they earn their place for routing.

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?

For a read-only lookup with full schema coverage and no output schema, the description covers purpose, triggering intents, and the item-name output variation adequately. It could say more about what the policy payload contains, but nothing essential for correct invocation is missing.

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?

Schema description coverage is 100%, covering store, item and country, including the country code's effect on policy selection and price links. The description's note that an item name adds price links largely restates what the schema's item description already states, so baseline 3 applies.

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 gives a specific verb-and-resource: it retrieves a store's price adjustment and price match policy. The sample queries ("does Target do price matching?", "Currys price promise") make the exact subject matter unmistakable and distinguish it from the write/claim-oriented siblings check_price_drop_claim and get_claim_reminder.

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?

The five example requests clearly map real user intents to this tool, so an agent knows exactly which questions select it. It stops short of naming alternatives (e.g. list_stores for store discovery) or stating exclusions, so it is clear context rather than full when/when-not guidance.

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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