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datasets_starbucks_stores_facets

Get counts of Starbucks stores grouped by country, state, market, amenities, or ownership type. Filter by city, state, market, amenity, or search query.

Instructions

Facet stored Starbucks stores. Returns terms aggregation counts for the Starbucks store directory. Facet enum: country, state, market, amenities, ownership_type_code. Accepts the same filter parameters as search to scope the aggregation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text search over store name, city, and address
cityNoExact city filter
facetYesFacet enum: country, state, market, amenities, ownership_type_code
stateNoState/region code filter
marketNoCrawl-provenance market filter. One of: us, ca
amenityNoAmenity code filter, e.g. DT
countryNoISO-3166-1 alpha-2 country filter

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "country",
+  "state",
+  "market",
+  "amenities",
+  "ownership_type_code"
+]
    • addedInput schema / properties / market / enum
      Added value: +[
+  "us",
+  "ca"
+]
  2. Addedv1.16.2

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that the tool returns terms aggregation counts and that it accepts filter parameters, which is useful. However, it doesn't disclose details like whether the aggregation is scoped globally or per filter, what the response structure looks like, or any rate limits. It's adequate but not rich.

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?

The description is two sentences, front-loads the core purpose, and lists the facet enum inline. It's efficient and every sentence earns its place. Minor deduction for not using a more structured format, but it's appropriately sized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a facet/aggregation tool with 7 parameters and no output schema, the description covers the core behavior and scoping mechanism. However, it doesn't explain what the response looks like (e.g., bucket keys, doc counts), which an agent might need to interpret results. It's complete enough for basic invocation but lacks return-format context.

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 parameters. The description adds the key insight that filter parameters (q, city, state, market, amenity, country) are used to scope the aggregation, which is not obvious from the schema alone. However, it doesn't add syntax or format details beyond that, so it's a baseline 3.

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 verb ('Facet') and resource ('stored Starbucks stores'), and clarifies it returns terms aggregation counts for the Starbucks store directory. It lists the facet enum values, which helps distinguish it from sibling tools like datasets_starbucks_stores_search and datasets_starbucks_stores_item. However, it doesn't explicitly name sibling tools or contrast with them, so it's clear but not fully differentiated.

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 says it 'Accepts the same filter parameters as search to scope the aggregation,' which implies when to use it: when you need aggregated counts rather than raw search results. It doesn't explicitly state when not to use it or name alternatives, but the context of 'facet' vs 'search' is clear enough for an agent to infer the appropriate use case.

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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