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JavaScript
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datasets_google_map_facets

Get aggregated counts for Google Maps businesses by category, country, state, county, city, town, or website status to explore and refine datasets.

Instructions

Facet stored Google Maps businesses. Returns terms aggregation counts for Google Maps businesses. Facet enum: category, country, state, state_code, county, county_code, city, town, website_status. Category facet values are exact locale-specific Google Maps labels and can be localized, non-ASCII, or contain punctuation; pass a returned value unchanged to the category filter. state and county keep each country's own administrative vocabulary (e.g. Provincia de Madrid, Département du Nord); state_code and county_code are the matching ISO 3166-2 codes (e.g. ES-MD, FR-59) and are the stable cross-country grouping key. Rows not yet resolved carry no code and are absent from the code facets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text business search query, max 256 characters
latNoLatitude for radius filtering
lonNoLongitude for radius filtering
cityNoExact city filter, max 128 characters
sortNoSort enum: relevance, updated_at_desc, rating_desc, review_count_desc, distance_asc
townNoExact town filter, max 128 characters
facetYesFacet enum: category, country, state, state_code, county, county_code, city, town, website_status
stateNoExact state filter, max 128 characters
countyNoExact county filter, max 128 characters
countryNoCountry filter. Accepts the full English name (\
has_geoNoFilter by location presence: true keeps only mappable businesses with coordinates; false isolates locationless service-area businesses that have no map location
categoryNoExact locale-specific Google Maps category label; use the category facet to discover values, max 128 characters
radius_mNoRadius in meters, 1 through 50000; requires lat and lon when supplied
has_phoneNoFilter by phone presence
min_ratingNoMinimum rating, 0 through 5. Businesses with no aggregate Google rating are returned with rating null, so any min_rating above 0 excludes them.
state_codeNoExact ISO 3166-2 state/region code filter, e.g. US-CA or FR-HDF, max 128 characters; use facet=state_code to discover values
county_codeNoExact ISO 3166-2 county/district code filter, e.g. FR-59 or IT-RM, max 128 characters; use facet=county_code to discover values
has_websiteNoFilter by website presence
min_review_countNoMinimum review count
permanently_closedNoClosure filter. true keeps only businesses Google marks Permanently closed; false excludes them, keeping every business not known to be closed. Most rows have never been status-checked and are returned with permanently_closed null, which means unknown, not open.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "category",
+  "country",
+  "state",
+  "state_code",
+  "county",
+  "county_code",
+  "city",
+  "town",
+  "website_status"
+]
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "relevance",
+  "updated_at_desc",
+  "rating_desc",
+  "review_count_desc",
+  "distance_asc"
+]
  2. Changed5 schema fields changedv1.16.2
    • changedInput schema / properties / country / description
      Previous value: -"Exact country filter, max 128 characters"New value: +"Country filter. Accepts the full English name (\\"
    • addedInput schema / properties / county_code
      Added value: +{
+  "description": "Exact ISO 3166-2 county/district code filter, e.g. FR-59 or IT-RM, max 128 characters; use facet=county_code to discover values",
+  "type": "string"
+}
    • changedInput schema / properties / facet / description
      Previous value: -"Facet enum: category, country, state, county, city, town, website_status"New value: +"Facet enum: category, country, state, state_code, county, county_code, city, town, website_status"
    • addedInput schema / properties / permanently_closed
      Added value: +{
+  "description": "Closure filter. true keeps only businesses Google marks Permanently closed; false excludes them, keeping every business not known to be closed. Most rows have never been status-checked and are returned with permanently_closed null, which means unknown, not open.",
+  "type": "boolean"
+}
    • addedInput schema / properties / state_code
      Added value: +{
+  "description": "Exact ISO 3166-2 state/region code filter, e.g. US-CA or FR-HDF, max 128 characters; use facet=state_code to discover values",
+  "type": "string"
+}
  3. Changed1 schema field changed
    • changedInput schema / properties / category / description
      Previous value: -"Exact category filter: a Google Maps type token in lower-case snake_case (e.g. dentist, bus_stop), max 128 characters"New value: +"Exact locale-specific Google Maps category label; use the category facet to discover values, max 128 characters"
  4. Changed3 schema fields changedv1.2.0
    • changedInput schema / properties / category / description
      Previous value: -"Exact category filter, max 128 characters"New value: +"Exact category filter: a Google Maps type token in lower-case snake_case (e.g. dentist, bus_stop), max 128 characters"
    • addedInput schema / properties / has_geo
      Added value: +{
+  "description": "Filter by location presence: true keeps only mappable businesses with coordinates; false isolates locationless service-area businesses that have no map location",
+  "type": "boolean"
+}
    • changedInput schema / properties / min_rating / description
      Previous value: -"Minimum rating, 0 through 5"New value: +"Minimum rating, 0 through 5. Businesses with no aggregate Google rating are returned with rating null, so any min_rating above 0 excludes them."
  5. Addedv1.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and discloses important behavioral nuances: category values may be localized, non-ASCII, or contain punctuation; state/county use country-specific vocabulary while codes are ISO 3166-2; and unresolved rows are absent from code facets. These details go well beyond a simple 'returns counts' statement and help an agent anticipate real data 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?

The description is dense but well-structured: the first sentence states purpose, the second enumerates facets, and the remaining sentences explain critical value semantics. Every sentence earns its place, and there is no filler or repetition of schema content.

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?

For a 20-parameter tool with no annotations and no output schema, the description covers the most important semantic traps an agent would face: localized category labels, administrative vocabulary differences, code stability, and missing codes for unresolved rows. Combined with 100% schema coverage, an agent has enough to call the tool correctly.

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 value beyond the schema by explaining relationships between parameters: category values are locale-specific and must be passed unchanged, state/county keep native vocabulary while state_code/county_code are stable ISO grouping keys. This gives context the schema alone does not fully convey.

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 clearly states the tool facets stored Google Maps businesses and returns terms aggregation counts. It distinguishes itself from sibling tools like datasets_google_map_search, datasets_google_map_nearby, and datasets_google_map_item by making the aggregation/count behavior explicit.

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 gives clear context on when to use this tool: when you need facet counts and want to discover valid filter values. It even provides workflow guidance by stating that category facet values can be passed unchanged to the category filter. However, it never names alternatives like datasets_google_map_search or datasets_google_map_nearby, nor states when not to use this tool.

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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