Query locations (paid)
query_locationsRead-only
Return matching rows from one dataset, filtered on ANY of its columns — state/city/county/zip shortcuts plus where conditions with numeric comparisons (e.g. [{field:"revenue_amt",op:"gt",value:2000000}]), sorted with order_by and paged with offset. near ({place:"Topeka, KS"}, a zip, or lat+lng, optional radius_miles or drive_minutes) returns the closest rows first with distance_miles, on lists with coordinates — so "10 banks closest to Topeka" is one call for 10 rows. get_dataset lists the columns; count_locations (free) tells you how many rows match and what fetching them costs before you pay. Priced per row in USDC via x402 and settled only after the rows are produced, so a failed call costs nothing. The rate is derived from the dataset: roughly 2x its list price spread over its record count, so a small slice of a big file is cents. By default you get and pay for every matching row, up to 100 to 1,000 rows per call depending on how wide the dataset's rows are (count_locations reports maxRowsPerCall); pass limit for fewer. Call it without payment first: the result is an x402 PaymentRequired quote with the exact amount and quote (the same rows, each source at its rate, as count_locations' slice), and nothing is charged until you retry with payment. A paid call is billed for the rows it returns: each row at its own list's per-row rate (an Overture open-data row at $0.005; a row of a chain LocationLists sells its own list for, at that list's rate), plus a $0.01 per-call fee, rounded once to the nearest cent, at least $0.02, never more than the whole list. count_locations with the same filters, limit and offset quotes exactly that page, row source by row source (price.breakdown), before anything is paid. How a list is sold: our own lists are sold as a whole file (create_checkout / buy_dataset), and those of 5,000 records or more are also sold by the row (query_locations / create_query_checkout); smaller lists are sold whole only. Overture Maps lists (slugs starting overture-) are sold by the row ONLY, at any size — there is no file to buy. To cover several chains near one place, pass datasets or category and a total (up to 1,000 rows) instead of dataset: one answer, one price and one file, with source_dataset naming each row's dataset and duplicates removed; inside a combined answer, small datasets are sold by the row too.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| zip | No | Shortcut for where zip eq <value> | |
| city | No | Shortcut for where city eq <value> | |
| near | No | Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 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). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. 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). | |
| limit | No | Rows to return and pay for. Default: every matching row, up to the most one call can return. That maximum depends on how wide the dataset's rows are, from 100 to 1,000; count_locations reports it as maxRowsPerCall, and a larger limit is reduced to it before pricing. | |
| metro | No | 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]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase. | |
| shape | No | CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank. | |
| state | No | Shortcut for where state eq <value>. Two-letter code. | |
| total | No | With datasets or category: rows to return and pay for across all of them (default every distinct match, up to 1,000), in one payment | |
| where | No | Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds. | |
| county | No | Shortcut for where county eq <value> | |
| offset | No | Skip this many matching rows, to page past the first call | |
| relate | No | ||
| dataset | No | Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead. | |
| exclude | No | With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded) | |
| permits | No | Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page. | |
| areas_in | No | Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area. | |
| category | No | Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these) | |
| datasets | No | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) | |
| order_by | No | Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last. | |
| area_where | No | 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), …. | |
| area_columns | No | 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). |