LocationLists

US business location data: search, sample, count, query rows or buy CSVs (Stripe or x402 USDC).

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What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    US business location data: search, sample, count, query rows or buy CSVs (Stripe or x402 USDC).

    How to connect

    How to connect LocationLists to Claude

    The server address to paste into Claude settings:

    https://locationlists.com/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect LocationLists to ChatGPT

    The server address to paste into ChatGPT settings:

    https://locationlists.com/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect LocationLists to Cursor

    The server address to paste into Cursor settings:

    https://locationlists.com/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (16)

    Raw names from tools/list. Only developers need these.

    search_datasetsFind 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 dataset of that kind, and `kinds` names it as a category that count_locations can combine into one answer. Each brand or chain is its own dataset. After finding one you can filter it by city, state, zip, any column, or a radius around a place (e.g. within 25 miles of Los Angeles, CA) when it has coordinates: use count_locations for how many match, and get_sample with the same filters to preview matching rows. Both are free. To cover several chains near one place, pass datasets or category and a total to count_locations, and you get one answer, one price and one file.
    relate_locationsFree. 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_miles), none_within (base rows with no anchor within radius_miles). Returns counts for both sets (rows without coordinates are left out and counted), summary stats, up to 3 preview rows, the price of the full answer (base rows plus the anchor rows named, each at its dataset's per-row rate, one card fee) and how to buy it with query_locations or create_query_checkout using the same arguments. Example: {"dataset": "<slug>", "state": "VA", "relate": {"mode": "nearest", "k": 1, "anchor": {"dataset": "<other slug>"}}}.
    count_by_areaFree, 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 placed are counted, never read as zero. Example: {"by": "county", "sets": [{"label": "a", "dataset": "<slug>"}, {"label": "b", "dataset": "<other slug>"}], "has": ["a"], "lacks": ["b"], "in_state": "VA"}.
    cotenancyFree, 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": "<slug>"}, "b": {"dataset": "<other slug>"}, "radius_miles": 1, "by": "county"}.
    get_datasetFull record for one dataset: fields with descriptions, record and state counts, coverage, 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 locationlists.com.
    count_locationsFree. 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 holds. Returns the exact card price of the matching rows, a link where the user can see and buy them, and the same rows in a cheaper list when one has them. Works for geography: city, state, county, zip, or `near` a place ("Los Angeles, CA"), zip or lat/lng within radius_miles or drive_minutes, on lists with coordinates. get_sample takes the same filters to preview matching rows. The result's `next` says exactly how to get every matching row. To cover several chains near one place, pass datasets or category (e.g. "retail" or "restaurant") and a total instead of dataset: one answer with counts per dataset, duplicates removed and up to 3 preview rows, one price and one file. Use get_dataset first for the column names. Scans the live file, so it can take several seconds on large datasets.
    get_sampleFree. 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, `where` on any column, or `near` a place such as {place: "Los Angeles, CA", radius_miles: 25} or {place: "Richmond, VA", drive_minutes: 30} on lists with coordinates): how many rows match, plus up to 3To preview several chains at once, pass datasets or category (e.g. "retail" or "restaurant") instead of slug: one combined answer with counts per dataset, duplicates removed and up to 3 rows across them.
    request_listAsk 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. Called with neither email nor email_declined, it sends nothing and asks for the email. The request goes to the LocationLists team, the same place as the request box on locationlists.com. We add new datasets every day and prioritize requested ones; there is no promised date. Free, nothing is charged.
    send_feedbackSend 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.
    email_quoteFree. 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 send it. Pass an email only if the user gave it to you in this conversation; never guess or invent one. Nothing is charged and nothing is bought; the price is checked again when they open the link.
    get_quoteLine-item prices and total for a list of dataset slugs. If a bundle covers several requested brands for less, it says so.
    create_checkoutOpens 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.
    check_orderGiven 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.
    create_query_checkoutFor 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 same per-row price query_locations charges, plus a card processing fee (2.9% + $0.30) added on top and shown separately. After payment the buyer is emailed a CSV download link; check_order with the session id returns it too. No match, a bad column, a distance search on a list without coordinates, or a subset that would cost more than the whole file returns an explanation and creates no checkout — nothing is charged. Agents with a USDC wallet should call query_locations instead. To cover several chains near one place, pass datasets or category and a total instead of dataset: one answer, one price and one checkout for one CSV (source_dataset names each row's dataset, duplicates removed).
    query_locationsReturn 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 nothing is charged until you retry with payment. Datasets under 5,000 records are not sold by the row — buy those files outright. 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.
    buy_datasetBuy 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.\n\nPrefer 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 buying outright is both cheaper and complete. get_dataset (free) gives the price and record count first.
    LocationLists: connect to Claude, ChatGPT, Cursor · Connectors.fun