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datasets_starbucks_stores_search

Search Starbucks stores worldwide by location, text, or amenity filters to find store details like address, hours, and pickup options.

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).

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

A4.1/5.0
Behavior4/5

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

Annotations are absent, so the description carries full burden. It provides important caveats: 'empty schedule means not published, not closed', 'country is true country, market is crawl provenance', and notes data limitations across markets. This is rich behavioral context.

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?

One dense paragraph, but it efficiently packs key information in a logical order: what, dataset details, data caveats, filters. No fluff, but it could be broken into shorter sentences for readability.

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?

Given the 12 params and no output schema, the description covers essential aspects: global scope, data gaps, filter options, and sort behavior. It does not explain pagination limits beyond schema, but that's covered in the schema. It's complete for a search tool.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description reinforces what the schema states (e.g., filters, sort) but does not add significant new meaning beyond clarifying 'country vs market' distinction, which is useful.

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?

Verb is 'Search', resource is 'Starbucks store directory', and the dataset id is specified. The description enumerates the fields and filters clearly, distinguishing it from sibling tools like 'datasets_starbucks_stores_item' and 'datasets_starbucks_stores_nearby'.

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 description explains when to use radius filtering, sort options, and full-text search, but does not explicitly state when not to use this tool. It implies the search is for broad store discovery, which is distinguishable from item-specific lookup.

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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