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Which shops carry a brand, and how honest their sales are

where_to_buy
Read-onlyIdempotent

For a brand or a country: the shops we read that carry it, ranked by how many genuine deals they actually have, plus how often they badge a discount versus how often that discount survives comparison.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNoBrand name. Either brand or country is required.
limitNoHow many results (default 10, max 50).
countryNoTwo-letter country code, e.g. "US", "CA", "DE".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and non-open-world, so the safety profile is covered. The description adds real behavioral context: results are ranked by count of genuine deals and include a badge-vs-survival discount honesty ratio, which is not derivable from structured fields.

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?

A single dense sentence that front-loads the scoping clause ('For a brand or a country') before the ranking details. It is efficient, though the trailing clause about badge-vs-survival metrics takes a moment to parse.

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?

With no output schema, the description carries the return-shape burden and mostly succeeds by naming the ranked entity and the two metrics returned. It remains slightly vague on pagination/limit behavior and on what happens when both brand and country are supplied.

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 coverage is 100% and all three parameters are documented in the schema, including the either/or requirement on brand and country. The description only restates the brand-or-country dimension and adds no format, default, or limit detail, so baseline 3 applies.

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

Purpose4/5

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

The description states a specific resource (shops carrying a brand/country) and the ranking criterion (genuine deals, discount-badge honesty), which is distinctive versus siblings like todays_deals or price_history. It does not explicitly name a sibling or contrast with fake_sales, which it partially overlaps, so it falls short of a 5.

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

Usage Guidelines3/5

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

Usage is implied: query with a brand or a country to see which retailers carry it. There is no explicit when-to-use, when-not-to-use, or routing to an alternative such as fake_sales for discount-honesty questions, so it is minimum viable.

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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