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datasets_starbucks_stores_search

Search the Starbucks store directory. Searches the worldwide Starbucks store directory (dataset id starbucks-stores), built by grid-tiling the store locator around its 50-result cap.

Instructions

Search the Starbucks store directory. Searches the worldwide Starbucks store directory (dataset id starbucks-stores), built by grid-tiling the store locator around its 50-result cap. Each store has its store number, name, phone, full address, coordinates, weekly hours, amenity codes, and pick-up options. Store discovery is global, but hours, amenities, and phone numbers are populated per market and are largely absent outside the US, Canada, Europe, and the Gulf; an empty schedule means "not published for this market", not "closed". country is the store's true country while market is crawl provenance (the US host geocodes worldwide). Supports full-text q, country/state/city/market/amenity filters, lat/lon/radius_m radius filtering, and sort (relevance, distance_asc). Billing: this search 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
qNoFull-text search over store name, city, and address, max 256 characters
latNoLatitude for radius filtering or distance sort, requires lon
lonNoLongitude for radius filtering or distance sort, requires lat
cityNoExact city filter
pageNoPage number, defaults to 1
sortNoSort enum: relevance, distance_asc
stateNoState/region code filter, e.g. WA
marketNoCrawl-provenance market filter. One of: us, ca
amenityNoAmenity code filter, e.g. DT (Drive-Thru), XO (Mobile Order and Pay)
countryNoISO-3166-1 alpha-2 country filter, e.g. US, GB, JP
radius_mNoRadius in meters, 1 through 50000; requires lat and lon
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / market / enum
      Added value: +[
+  "us",
+  "ca"
+]
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "relevance",
+  "distance_asc"
+]
  2. Addedv1.16.2

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations the description carries the full burden and delivers: the 50-result cap tiling, the caveat that hours/amenities/phone are largely US/Canada/Europe/Gulf only, that an empty schedule means 'not published', the country-vs-market provenance distinction, and the precise billing rule (1 credit/record, minimum 2, empty result = 2).

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 the core purpose in the first sentence, and nearly every clause (billing, data gaps, country/market) earns its place. It is nonetheless a dense single block that runs long for a tool definition.

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?

No output schema exists, and the description compensates by enumerating the returned fields (store number, name, phone, address, coordinates, hours, amenity codes, pick-up options), plus cost and data-coverage limits. Nothing an agent needs to call or interpret results is missing.

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

Parameters4/5

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

Schema coverage is 100% so the baseline is 3, but the description adds genuine meaning: it clarifies that country is the store's true country while market is crawl provenance (the US host geocodes worldwide), resolving an ambiguity the schema only hints at. Minor gain over structured fields beyond that.

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 and resource ('Search the Starbucks store directory') and names the underlying dataset id, then enumerates exactly what a store record contains. An agent can distinguish it from datasets_starbucks_stores_nearby/item/facets without opening a schema.

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?

It lists the available filter modes (full-text, geo, country/state/city/market/amenity, radius, sort), which implies how to use it, but never states when this is preferable to the sibling datasets_starbucks_stores_nearby tool, which also does radius filtering. No explicit when/when-not routing is provided.

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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