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datasets_starbucks_stores_nearby

Find nearby stored Starbucks stores. Returns stored Starbucks stores within a radius of a point, nearest first, from dataset id starbucks-stores. lat, lon, and radius_m are required.

Instructions

Find nearby stored Starbucks stores. Returns stored Starbucks stores within a radius of a point, nearest first, from dataset id starbucks-stores. lat, lon, and radius_m are required. Unlike the live /starbucks/stores endpoint (which caps at 50 near a point), this queries the full grid-tiled directory, so it can return every store in the radius. Billing: this endpoint costs 1 credit per record returned, with a minimum of 2 (an empty result costs 2, a full page of 100 costs 100).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesCenter latitude, from -90 through 90
lonYesCenter longitude, from -180 through 180
pageNoPage number, defaults to 1
amenityNoAmenity code filter, e.g. DT
countryNoISO-3166-1 alpha-2 country filter
radius_mYesSearch radius in meters, 1 through 50000
page_sizeNoPage size, defaults to 20 and maxes at 100

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.16.2

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the load and does so well: it discloses result ordering (nearest first), the underlying dataset id, the completeness guarantee versus the capped live endpoint, and full billing semantics (1 credit/record, minimum 2, empty=2, 100=100). It omits auth or rate-limit behavior, but the cost and completeness disclosures are materially useful.

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?

Front-loaded with purpose, then dataset id, required params, the comparison to the live endpoint, and billing. Every sentence earns its place, with only the required-params line being mildly redundant against the schema.

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 geospatial search with no output schema, it conveys return ordering, completeness, and cost — the key facts an agent needs. Pagination behavior is left to the page/page_size schema fields, so nothing critical 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%, so the schema already documents all 7 parameters. The description only restates that lat/lon/radius_m are required, which the schema's required list already conveys, adding no new syntax or constraint detail. Baseline 3 is appropriate.

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?

States a specific verb (Find) and resource (nearby stored Starbucks stores) with a clear geospatial scope, and explicitly contrasts itself with the live /starbucks/stores endpoint. An agent can distinguish it from the search/item/facets siblings without opening any schema.

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?

Names the alternative endpoint (/starbucks/stores) and the condition that selects this tool (need full directory, not the 50-result cap). Clear context for choosing it, though it doesn't route the agent away from or toward the sibling datasets_starbucks_stores_search.

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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