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LocationLists

by io.github.kylehawke-stack
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Cotenancy between two sets (free)

cotenancy
Read-onlyIdempotent

Free, counts only. How two sets of places sit together: the share of set a within radius_miles of set b and of b within radius_miles of a, how many places overlap, and the county / zip / state / metro areas that have both, only a, or only b (top 10 of each named). Each set is a dataset, datasets or category plus filters, the same as count_locations; any US brand or kind of place works, including Overture lists from search_datasets. Example: {"a": {"dataset": ""}, "b": {"dataset": ""}, "radius_miles": 1, "by": "county"}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aYesFirst set: dataset, datasets or category, plus filters
bYesSecond set, given the same way
byNoArea unit for both/only-a/only-b (default county)
in_stateNoOnly areas in this state
radius_milesNoDistance that counts as together (default 1 mile)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changed
    • addedInput schema / properties / a / properties / areas_in
      Added value: +{
+  "additionalProperties": false,
+  "description": "Only rows in these areas, e.g. {\"by\": \"county\", \"ids\": [\"51760\"]} for Richmond city, VA — the ids count_by_area returns, placed the same way (/find: areas_in=county:51760).",
+  "properties": {
+    "by": {
+      "enum": [
+        "county",
+        "zip",
+        "state",
+        "metro"
+      ],
+      "type": "string"
+    },
+    "ids": {
+      "description": "County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row",
+      "items": {
+        "type": "string"
+      },
+      "maxItems": 200,
+      "minItems": 1,
+      "type": "array"
+    }
+  },
+  "required": [
+    "by",
+    "ids"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / a / properties / metro
      Added value: +{
+  "description": "Only rows in one metro area (CBSA): its 5-digit code (\"37980\") or its name (\"Philadelphia\", \"Philadelphia, PA\"). The same as areas_in {\"by\": \"metro\", \"ids\": [code]}.",
+  "type": "string"
+}
    • changedInput schema / properties / a / properties / near / properties / drive_minutes / description
      Previous value: -"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."
    • addedInput schema / properties / a / properties / opendata
      Added value: +{
+  "additionalProperties": false,
+  "description": "INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state's licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher's own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {\"dataset\": \"ymca\", \"state\": \"NY\", \"relate\": {\"mode\": \"near\", \"near_m\": 30, \"anchor\": {\"opendata\": {\"source\": \"data.ny.gov/cb42-qumz\", \"state\": \"NY\"}}}}.",
+  "properties": {
+    "source": {
+      "description": "The register's key, \"<domain>/<id>\" — search_datasets with kind \"register\" finds it, e.g. \"data.ny.gov/cb42-qumz\" (New York's licensed child-care programs)",
+      "type": "string"
+    },
+    "state": {
+      "description": "Cut the register to one state, e.g. \"NY\" (/find: opendata_state=NY)",
+      "type": "string"
+    },
+    "where": {
+      "description": "Conditions on the register's own columns, each \"column:op:value\" with op eq | starts_with | contains | not_blank, e.g. [\"license_status:starts_with:Active\"] (/find: opendata_where=)",
+      "items": {
+        "type": "string"
+      },
+      "maxItems": 12,
+      "type": "array"
+    },
+    "zips": {
+      "description": "Or cut it to 5-digit ZIP codes, e.g. [\"78114\", \"78154\"] (/find: opendata_zips=78114,78154)",
+      "items": {
+        "type": "string"
+      },
+      "minItems": 1,
+      "type": "array"
+    }
+  },
+  "required": [
+    "source"
+  ],
+  "type": "object"
+}
    • addedInput schema / properties / b / properties / areas_in
      Added value: +{
+  "$ref": "#/properties/a/properties/areas_in"
+}
    • addedInput schema / properties / b / properties / metro
      Added value: +{
+  "$ref": "#/properties/a/properties/metro"
+}
    • addedInput schema / properties / b / properties / opendata
      Added value: +{
+  "$ref": "#/properties/a/properties/opendata",
+  "description": "INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state's licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher's own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {\"dataset\": \"ymca\", \"state\": \"NY\", \"relate\": {\"mode\": \"near\", \"near_m\": 30, \"anchor\": {\"opendata\": {\"source\": \"data.ny.gov/cb42-qumz\", \"state\": \"NY\"}}}}."
+}
  2. Changed1 schema field changed
    • changedInput schema / properties / a / properties / area_where / description
      Previous value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."
  3. Changed1 schema field changed
    • changedInput schema / properties / a / properties / area_where / description
      Previous value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."
  4. Changed3 schema fields changed
    • changedInput schema / properties / a / properties / area_columns / description
      Previous value: -"Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."
    • addedInput schema / properties / a / properties / where / items / properties / any_of
      Added value: +{
+  "items": {
+    "additionalProperties": false,
+    "properties": {
+      "field": {
+        "type": "string"
+      },
+      "op": {
+        "enum": [
+          "eq",
+          "ne",
+          "gt",
+          "gte",
+          "lt",
+          "lte",
+          "in",
+          "contains",
+          "starts_with",
+          "is_blank",
+          "not_blank",
+          "not_contains",
+          "not_in",
+          "any_of"
+        ],
+        "type": "string"
+      },
+      "value": {
+        "anyOf": [
+          {
+            "type": "string"
+          },
+          {
+            "type": "number"
+          },
+          {
+            "type": "boolean"
+          },
+          {
+            "items": {
+              "type": [
+                "string",
+                "number"
+              ]
+            },
+            "type": "array"
+          }
+        ]
+      }
+    },
+    "required": [
+      "field",
+      "op"
+    ],
+    "type": "object"
+  },
+  "maxItems": 12,
+  "type": "array"
+}
    • changedInput schema / properties / a / properties / where / items / properties / op / enum
      Previous value: -[
-  "eq",
-  "ne",
-  "gt",
-  "gte",
-  "lt",
-  "lte",
-  "in",
-  "contains",
-  "starts_with",
-  "is_blank",
-  "not_blank"
-]New value: +[
+  "eq",
+  "ne",
+  "gt",
+  "gte",
+  "lt",
+  "lte",
+  "in",
+  "contains",
+  "starts_with",
+  "is_blank",
+  "not_blank",
+  "not_contains",
+  "not_in",
+  "any_of"
+]
  5. Changed6 schema fields changed
    • addedInput schema / properties / a / properties / near / description
      Added value: +"Distance search on lists with coordinates: ONE of place, zip, lat+lng or points, with radius_miles or drive_minutes (not both)."
    • addedInput schema / properties / a / properties / near / properties / drive_minutes
      Added value: +{
+  "description": "Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free).",
+  "maximum": 240,
+  "minimum": 5,
+  "type": "integer"
+}
    • addedInput schema / properties / a / properties / near / properties / place / description
      Added value: +"City or town with state, e.g. \"Topeka, KS\""
    • addedInput schema / properties / a / properties / near / properties / points
      Added value: +{
+  "description": "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices.",
+  "items": {
+    "additionalProperties": false,
+    "properties": {
+      "lat": {
+        "$ref": "#/properties/a/properties/near/properties/lat"
+      },
+      "lng": {
+        "$ref": "#/properties/a/properties/near/properties/lng"
+      },
+      "place": {
+        "$ref": "#/properties/a/properties/near/properties/place"
+      },
+      "zip": {
+        "$ref": "#/properties/a/properties/near/properties/zip"
+      }
+    },
+    "type": "object"
+  },
+  "maxItems": 10,
+  "minItems": 1,
+  "type": "array"
+}
    • addedInput schema / properties / a / properties / near / properties / radius_miles / description
      Added value: +"Only rows within this straight-line distance of the point (or of any of the points). Free."
    • addedInput schema / properties / a / properties / near / properties / zip / description
      Added value: +"5-digit zip"
  6. Changed1 schema field changed
    • changedInput schema / properties / a / properties / area_where / description
      Previous value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."
  7. Changed3 schema fields changed
    • changedInput schema / properties / a / properties / area_where / description
      Previous value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."
    • addedInput schema / properties / a / properties / exclude
      Added value: +{
+  "description": "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded).",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
    • addedInput schema / properties / b / properties / exclude
      Added value: +{
+  "$ref": "#/properties/a/properties/exclude"
+}
  8. Changed4 schema fields changed
    • addedInput schema / properties / a / properties / area_columns
      Added value: +{
+  "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free.",
+  "items": {
+    "type": "string"
+  },
+  "maxItems": 6,
+  "type": "array"
+}
    • addedInput schema / properties / a / properties / area_where
      Added value: +{
+  "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+  "items": {
+    "type": "string"
+  },
+  "maxItems": 6,
+  "type": "array"
+}
    • addedInput schema / properties / b / properties / area_columns
      Added value: +{
+  "$ref": "#/properties/a/properties/area_columns"
+}
    • addedInput schema / properties / b / properties / area_where
      Added value: +{
+  "$ref": "#/properties/a/properties/area_where"
+}
  9. Added

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnly=true, idempotent=true, non-destructive, closed-world, so the safety profile is covered. The description adds useful context that it is free and returns counts only, plus the top-10 naming cap, but does not elaborate on the exact response shape or pricing detail beyond 'free'.

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-loads the key constraint ('Free, counts only') then the purpose, then the set-construction rule and an example. Well ordered and largely waste-free, though the enumeration of returned components is dense.

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?

With no output schema, the description steps in to explain the return contents (shares, overlap count, per-area both/only-a/only-b lists, top 10 named). Combined with exhaustive schema coverage, an agent has enough to call it correctly; only the precise numeric format of shares is left implicit.

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 five parameters in depth. The description's example (radius_miles, by) and its restatement of set structure add mild clarity but no syntax or semantics beyond what the schema provides; baseline 3 applies.

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 ('how two sets of places sit together') and enumerates exactly what it computes: the share of a within radius of b and vice versa, overlap counts, and the both/only-a/only-b area breakdown. This is clearly distinguishable from siblings like count_locations and relate_locations.

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?

Explains how each set is specified ('a dataset, datasets or category plus filters, the same as count_locations') and notes that any US brand or Overture list works, which routes the agent from search_datasets. It does not, however, explicitly state when to prefer this over relate_locations or count_by_area.

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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