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datasets_apple_maps_places_facets

Aggregate stored Apple Maps places by category, country, state, city, price symbol, or catalog country, applying search and location filters to return facet counts and reveal valid category names.

Instructions

Facet stored Apple Maps places. Returns terms aggregation counts for the stored Apple Maps place directory. Facet enum: category, country_code, state, city, price_symbol, catalog_country. Accepts the same filter parameters as search to scope the aggregation. Use facet=category to discover the exact category names the category filter accepts.

Input Schema

NameRequiredDescriptionDefault
qNoFull-text search over place name, category, city and address
latNoLatitude for radius filtering, requires lon
lonNoLongitude for radius filtering, requires lat
cityNoExact city filter
facetYesFacet enum: category, country_code, state, city, price_symbol, catalog_country
stateNoExact state/region filter
categoryNoExact Apple category name
radius_mNoRadius in meters, 1 through 50000; requires lat and lon
has_phoneNoPhone presence filter
min_ratingNoMinimum rating, from 0 through 5
has_websiteNoWebsite presence filter
postal_codeNoExact postal code filter
country_codeNoISO 3166-1 alpha-2 country filter
min_rating_countNoMinimum number of ratings behind the rating

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.16

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full disclosure burden. It does explain that the output is terms aggregation counts over stored Apple Maps places and that filters scope the aggregation, but it does not state read-only safety, data freshness, result limits, or authentication expectations.

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 front-loaded and compact, moving from operation to output to facet enum to scoping and one usage tip. It repeats the facet enum already present in the schema, which is mildly redundant but not disruptive.

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 14 parameters and no output schema, the description covers the core operation, output type, scoping behavior, and a key discovery use case. It could be more complete by describing the shape of the aggregation counts or limits, but it is sufficient for correct invocation.

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. The description adds meaningful semantics beyond the schema by stating that the filter parameters scope the aggregation and by explaining that `facet=category` can reveal the accepted category filter values.

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 and resource: faceting stored Apple Maps places, returning terms aggregation counts over the place directory. It is clearly distinguishable from a plain search by the aggregation-count output, though it only refers generically to "search" rather than naming the sibling tool.

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?

It explains that the aggregation can be scoped with the same filter parameters as search and gives a concrete usage case: `facet=category` to discover exact category names. It does not explicitly say when to prefer this tool over the search sibling, but the context is otherwise clear.

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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