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starbucks_stores

Find nearby Starbucks stores anywhere in the world. Enter a city, address, or coordinates to get store details, hours, amenities, and pickup options.

Instructions

Find nearby Starbucks stores worldwide. Returns Starbucks store locations near a point: store number, name, phone, full address, coordinates, weekly opening hours, amenities, and pick-up options. Either place, or both lat and lng, is required. place is free-text (city, address, or postal code) and is geocoded by Starbucks itself, so it works worldwide. market selects which Starbucks country site answers, one of us or ca, defaulting to us; this is not cosmetic even for stores, because the same store reports different operational data depending on the host. There is no filter parameter: Starbucks' own API accepts a features amenity filter but silently ignores it, so it is deliberately not offered here; filter on each store's returned amenities instead. A place Starbucks cannot resolve returns a well-formed empty result with place_not_found set to true rather than an error. The upstream returns at most 50 stores per request and supports no pagination; result_capped is true when that ceiling was reached. Store discovery works worldwide, but hours, amenities, and phone numbers are populated per market and may be absent outside the US and UK.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude, requires lng
lngNoLongitude, requires lat
placeNoFree-text city, address, or postal code, geocoded by Starbucks
marketNoStarbucks country site to read. One of: us, ca. Defaults to us

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / market / enum
      Added value: +[
+  "us",
+  "ca"
+]
  2. Addedv1.16.2

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it excels. It discloses the 50-store cap, absence of pagination, the result_capped flag, the place_not_found empty-result behavior, and market-dependent field availability (hours/amenities/phone absent outside US/UK). It also explains why the filter parameter is intentionally omitted, going beyond simple statements to explain upstream quirks.

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?

Every sentence in this description contributes actionable information. It is front-loaded with the core purpose, then systematically covers input requirements, parameter nuances, edge cases, and limitations. No filler or redundancy; 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?

The description covers all necessary aspects for an agent to use the tool correctly: input validation, output content, error handling, result limits, and market-specific data availability. Since there is no output schema, the description adequately lists returned fields and flags. It is complete for a tool of this complexity.

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?

Schema coverage is 100%, but the description adds critical semantics beyond the schema: the mutual exclusivity/requirement of place vs lat/lng, that place is geocoded by Starbucks and works worldwide, that market defaults to us and is not merely a locale preference but changes the data, and the absence of a filter parameter. These are essential for correct invocation and are not evident from the schema alone.

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 ('Find nearby Starbucks stores worldwide') and enumerates exactly what is returned (store number, name, phone, address, coordinates, hours, amenities, pick-up options). It is unambiguous and clearly distinct from sibling tools like starbucks_menu or starbucks_nearest_store, even without naming them explicitly.

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?

It explicitly states the required input combination (either place, or both lat and lng), explains that place is geocoded by Starbucks, clarifies the market parameter's significance (not cosmetic, affects operational data), and warns that there is no filter parameter because the upstream API ignores it. This gives an agent complete guidance on how to call the tool and what to expect.

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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