Model Context Protocol (MCP) server for Apollo.io API integration
Nothing to run from here
LocationLists is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
16
tools declared
—
trust score
remote
at its publisher
—
calls last 7d
What it does
US business location data: search, sample, count, query rows or buy CSVs (Stripe or x402 USDC).
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user --transport http locationlists-mcp https://locationlists.com/mcp
# 2 — ask your agent for something these tools do
# search_datasets, relate_locations, count_by_area and 13 more
LocationLists costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
KSAll 1 server
kylehawke-stack1 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. Nothing here runs on your machine and nothing runs on ours: this server answers at its publisher’s own address, and what you install is the connection straight to it.
Claude Codedeclared by this listing
This is a public server: it answers at its publisher’s own address and this marketplace is not in the path. Claude Code registers it in one command; --scope user makes it available in every project. The transport below is http, as the publisher's own manifest declares it.
claude mcp add --scope user --transport http locationlists-mcp https://locationlists.com/mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it answers at its publisher’s own address: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it writes
One config entry pointing straight at the publisher’s own address — this marketplace is not in the path, and nothing from this listing executes on your machine.
What you approve
16 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Find LocationLists datasets by brand, kind of business or industry (e.g. 'bobcat', 'restaurants', 'bank branches', 'dental practices', 'hardware stores'). Returns EVERY matching dataset, best first, with slug, name, business type, industry, record count, coverage, whether it can be searched by distance (distanceSearch), and page URL. A kind of business or an industry in the query matches every dat
| Parameter | Type | Required | Notes |
|---|---|---|---|
| query | string | no | Free text: brand, kind of business, industry or product |
| category | string | no | Restrict to one catalog category, industry or subcategory |
| limit | integer | no | Max results (default: every match) |
| kind | string | no | list (default): the lists we sell. register: live public registers read from their publisher, e.g. {"query": "child care", "kind": "register", "state": "NY"} — each result's source goes in relate_loca |
| state | string | no | With kind "register": prefer registers covering this state, e.g. "NY" |
Free. How one set of places relates to another, by straight-line distance. The base set is the usual dataset / datasets / category plus filters; relate.anchor is the other set, given the same way. Modes: nearest (each base row's k<=3 nearest anchors with miles), count_within (rank base rows by how many anchors are within radius_miles), within_any (base rows with at least one anchor within radius_m
| Parameter | Type | Required | Notes |
|---|---|---|---|
| dataset | string | no | — |
| datasets | array | no | — |
| category | string | no | — |
| exclude | array | no | 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). |
| state | string | no | — |
| city | string | no | — |
| county | string | no | — |
| zip | string | no | — |
| metro | string | 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]}. |
| areas_in | object | no | 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). |
| where | array | no | — |
| near | object | no | Distance search on lists with coordinates: ONE of place, zip, lat+lng or points, with radius_miles or drive_minutes (not both). |
| area_where | array | 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". Ever |
| area_columns | array | 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 / st |
| opendata | object | no | 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 |
| relate | object | yes | Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: r |
| total | integer | no | Base rows wanted, first in answer order |
Free, counts only. Counts places per county / zip / state / metro for 1 to 4 labeled sets (each a dataset, datasets or category plus filters), and compares them: has (areas with at least one of every listed set) and lacks (areas with none of any listed set). E.g. counties that have set a but no set b; ZIPs where a closure-filtered set exists and another set still has places. Rows that cannot be pl
| Parameter | Type | Required | Notes |
|---|---|---|---|
| by | string | yes | — |
| sets | array | yes | — |
| has | array | no | — |
| lacks | array | no | — |
| in_state | string | no | Only areas in this state |
| area_where | array | no | Only areas meeting a Census condition, each as "<kind>:<attribute><op><value>" about the same kind as by (or a state), e.g. "county:population>500000" with lacks for "counties over 500k people with no |
| per | number | no | Counts per this many residents of each area from its Census population (100000 adds per_100k to every area and ranks by it), or per this many of per_field. |
| per_field | string | no | With per: the Census count the rate is over instead of population, e.g. per=1000 with per_field=construction_establishments adds per_1000_construction_establishments. Any count attribute: population, |
| area_columns | array | no | Census and NOAA figures RETURNED on every area row (in attributes), each as "<kind>:<attribute>,<attribute>" about the same kind as by (or "state:…" for the area's state, returned as state_<attribute> |
| order_by | object | no | Rank the areas: {"field": "median_household_income"} for the richest counties first, {"field": "a", "direction": "asc"} for the fewest of set a first. Unknown figures sort last. |
| limit | integer | no | Areas listed per call (default: every matching area when 500 or fewer match, else the first 100; at most 1000) |
| offset | integer | no | Areas to skip before the first listed: page through every matching area with limit + offset. The answer's page block gives total and the next offset (null on the last page). |
| all | boolean | no | List every matching area, however many |
| top_values | object | no | The most common values of one class column per area, per set (top on each area row): what kinds of place make up a count — {"column": "category"} shows that Elkhart County's 47 trailer dealers are 30 |
| with_sample | boolean | no | Free. On the first 10 areas listed, each set's fixed published sample rows that fall inside the area (source_dataset, the list's columns, matches_your_question). The same published rows every question |
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 O
| Parameter | Type | Required | Notes |
|---|---|---|---|
| a | object | yes | First set: dataset, datasets or category, plus filters |
| b | object | yes | Second set, given the same way |
| radius_miles | number | no | Distance that counts as together (default 1 mile) |
| by | string | no | Area unit for both/only-a/only-b (default county) |
| in_state | string | no | Only areas in this state |
Full record for one dataset: fields with descriptions, record and state counts, coverage measured on the rows (a list in 14 states says "partial U.S.: 14 states"; coverageDetail lists the states, rows per state and the states with none), whether it can be searched by distance, advertised refresh cadence AND the real last-modified date of the file, FAQs, sample URL and the dataset's page on locatio
| Parameter | Type | Required | Notes |
|---|---|---|---|
| slug | string | yes | Dataset slug from search_datasets, e.g. bobcat-dealers |
Free. How many rows of one dataset match a filter — on geography AND any other column (e.g. nonprofits with revenue_amt gt 2000000, dealers with dealerClass eq 'Elite'). Also reports how many rows were excluded only because a tested column was blank, so a thin column is not mistaken for a small answer; a small or empty answer says how many rows each condition removed and what the column really hol
| Parameter | Type | Required | Notes |
|---|---|---|---|
| dataset | string | no | Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead. |
| state | string | no | Shortcut for where state eq <value>. Two-letter code. |
| city | string | no | Shortcut for where city eq <value> |
| county | string | no | Shortcut for where county eq <value> |
| zip | string | no | Shortcut for where zip eq <value> |
| where | array | 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 man |
| near | object | 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) |
| area_where | array | 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". Ever |
| area_columns | array | 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 / st |
| metro | string | 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_a |
| areas_in | object | 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 a |
| datasets | array | no | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) |
| category | string | 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) |
| exclude | array | 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 | object | 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": "c |
| total | integer | no | With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches |
| limit | integer | no | Price a slice instead of every match: the first limit rows past offset in the delivery order — e.g. near a place with limit 10 for the 10 nearest — exactly the rows a purchase with the same limit |
| offset | integer | no | With limit: skip this many matching rows, as a paged purchase does, so the quote is for that page |
| order_by | object | no | With limit: the order the slice is taken in, as a purchase takes it. Without it, nearest first with near, else file order. |
Free. Real rows from the live file, as JSON plus CSV text. Show these to the user so they can judge the fields and quality. Without filters: up to 10 rows spread across the whole dataset. With filters (the same ones count_locations takes: city, state, county, zip, metro, where on any column, areas_in (county / ZIP / metro / state ids from count_by_area), area_where, area_columns (Census figures
| Parameter | Type | Required | Notes |
|---|---|---|---|
| slug | string | no | Dataset slug. Or datasets / category to preview several at once. |
| rows | integer | no | Rows to return (default 10; with filters default 3, up to the published sample's size — samplePublished in the answer) |
| state | string | no | Shortcut for where state eq <value>. Two-letter code. |
| city | string | no | Shortcut for where city eq <value> |
| county | string | no | Shortcut for where county eq <value> |
| zip | string | no | Shortcut for where zip eq <value> |
| where | array | 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 man |
| near | object | 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) |
| area_where | array | 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". Ever |
| area_columns | array | 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 / st |
| metro | string | 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_a |
| areas_in | object | 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 a |
| datasets | array | no | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) |
| category | string | 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) |
| exclude | array | 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 | object | 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": "c |
| total | integer | no | With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches |
| shape | string | 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 s |
Ask LocationLists to add a list we do not have yet. Use it when search_datasets finds nothing that fits, or the user wants a brand, place or kind of business we do not publish. BEFORE calling: ask the user whether to send the request, and ask for their email so we can tell them when the list is ready. Pass an email only if the user gave it to you in this conversation; never guess or invent one. Ca
| Parameter | Type | Required | Notes |
|---|---|---|---|
| topic | string | yes | The list the user wants, in their words (2-200 characters) |
| string | no | The user's email, only if they gave it, so we can tell them when the list is ready | |
| email_declined | boolean | no | True only when you asked the user for their email and they chose not to leave one |
| use_case | string | no | What they will use the list for, if they said (e.g. sales outreach, territory planning) |
| details | string | no | Anything else they specified: places, fields needed (phone, website, email), timing |
| name | string | no | The user's name, if they gave it |
| company | string | no | The user's company, if they gave it |
Send a message to the LocationLists team: wrong or missing data in a dataset, something that did not work, a pricing question, an idea, or anything else. Ask the user before sending and use their words. Ask for their email so the team can reply, and pass it only if they gave it; never guess or invent one. Called with neither email nor email_declined, it sends nothing and asks for the email. Free.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| message | string | yes | The feedback, in the user's words (5-4000 characters) |
| string | no | The user's email, only if they gave it | |
| email_declined | boolean | no | True only when you asked the user for their email and they chose not to leave one |
| category | string | no | What it is about (default other) |
| dataset | string | no | Dataset slug it concerns, if any, e.g. generac-dealers |
Free. Emails the user a plain-English quote for exactly this request: how many rows match, the card price, a few of the matches and a card checkout link, so they can pay later, from any device, or forward it to whoever holds the card. Takes the same arguments as count_locations: dataset, or datasets / category with total, plus filters. BEFORE calling: ask the user for their email and whether to se
| Parameter | Type | Required | Notes |
|---|---|---|---|
| string | yes | The user's email, as they gave it | |
| dataset | string | no | Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead. |
| state | string | no | Shortcut for where state eq <value>. Two-letter code. |
| city | string | no | Shortcut for where city eq <value> |
| county | string | no | Shortcut for where county eq <value> |
| zip | string | no | Shortcut for where zip eq <value> |
| where | array | 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 man |
| near | object | 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) |
| area_where | array | 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". Ever |
| area_columns | array | 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 / st |
| metro | string | 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_a |
| areas_in | object | 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 a |
| datasets | array | no | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) |
| category | string | 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) |
| exclude | array | 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 | object | 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": "c |
| total | integer | no | With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches |
Line-item prices and total for a list of dataset slugs, each with how it is sold (soldBy: file, row, or both) — the same rule create_checkout, buy_dataset, create_query_checkout and query_locations enforce, so a quote never offers a route checkout refuses. A list sold by the row only (an Overture Maps list) is quoted at its per-row rate with the route named, not as a file; count_locations with fil
| Parameter | Type | Required | Notes |
|---|---|---|---|
| slugs | array | yes | Dataset slugs |
Opens a Stripe Checkout session for one dataset and returns the payment URL plus the session id. Give the URL to the user to pay (card, Apple Pay, Google Pay). After payment Stripe emails them a permanent download link; use check_order with the session id to confirm and fetch it. Does not charge anything by itself.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| slug | string | yes | Dataset slug |
| string | no | Buyer email, if known — prefills Checkout and is where the download link is sent |
Given a Stripe Checkout session id (cs_…), reports whether it is paid and, if so, returns the permanent download link for the CSV. Works for whole files, filtered rows and combined (several-dataset) orders. Any download link takes ?shape=hubspot or ?shape=salesforce for CRM-ready column names (every column kept).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| sessionId | string | yes | Checkout session id from create_checkout |
For buyers paying by card (no wallet needed): opens a Stripe Checkout for just the rows of one dataset that match a filter, and returns the payment URL to give the user. Takes the same filters as count_locations (state/city/county/zip, where on any column, near, order_by) and up to 10,000 rows. It counts the matches first, so the buyer pays only for rows that exist: the data price is the sam
| Parameter | Type | Required | Notes |
|---|---|---|---|
| dataset | string | no | Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead. |
| state | string | no | Shortcut for where state eq <value>. Two-letter code. |
| city | string | no | Shortcut for where city eq <value> |
| county | string | no | Shortcut for where county eq <value> |
| zip | string | no | Shortcut for where zip eq <value> |
| where | array | 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 man |
| near | object | 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) |
| area_where | array | 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". Ever |
| area_columns | array | 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 / st |
| metro | string | 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_a |
| areas_in | object | 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 a |
| datasets | array | no | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) |
| category | string | 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) |
| exclude | array | 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 | object | 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": "c |
| total | integer | no | With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches |
| relate | object | no | — |
| shape | string | 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 s |
| order_by | object | no | Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last. |
| limit | integer | no | Most rows to buy (max 10,000). Default: every match up to the max. Priced on the rows that actually match, never more. |
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 l
| Parameter | Type | Required | Notes |
|---|---|---|---|
| dataset | string | no | Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead. |
| state | string | no | Shortcut for where state eq <value>. Two-letter code. |
| city | string | no | Shortcut for where city eq <value> |
| county | string | no | Shortcut for where county eq <value> |
| zip | string | no | Shortcut for where zip eq <value> |
| where | array | 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 man |
| near | object | 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) |
| area_where | array | 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". Ever |
| area_columns | array | 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 / st |
| metro | string | 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_a |
| areas_in | object | 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 a |
| datasets | array | no | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) |
| category | string | 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) |
| exclude | array | 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 | object | 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": "c |
| total | integer | 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 |
| relate | object | no | — |
| shape | string | 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 s |
| order_by | object | no | Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last. |
| offset | integer | no | Skip this many matching rows, to page past the first call |
Buy an ENTIRE dataset outright and get a permanent download link for the CSV. Pays once in USDC on Base, at the same list price a human pays by card — no account and no checkout page. Prefer this over repeated query_locations calls whenever you want most of a file. Metered queries are priced per row and deliberately cost more than the file if you assemble it that way, so past a few hundred rows
| Parameter | Type | Required | Notes |
|---|---|---|---|
| dataset | string | yes | Dataset slug, e.g. generac-dealers. Use search_datasets first. |
Free
$0 forever
- Every tool — any call limit is its publisher’s, not ours
- No card, and mcprush charges nothing for it
- Runs at its publisher’s own address — this marketplace is not in the path
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install LocationLists or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
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Alert at 80% · then the tool returns a normal MCP error
Used this month: —
—Grade not published
No rule-by-rule report on file for this release
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
This listing is an address its publisher runs, and you connect to it directly — this marketplace is not in the path. The scanner reads packages and public source, and an address is neither: the repository linked on this page is where this record was collected from, not a copy of what the address serves, so it is not graded in its place. So there is no grade — not a withheld one, an unmeasured one.
Release history
This is an address its publisher runs: which release it serves is theirs to choose, so there is nothing here to pin. The history below is for reading; the current release on file is 1.2.0.
1.2.0
undated
latestNo release note was published with this version.
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no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish LocationLists can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.