bots.st/locationlists
LocationLists
US business location data search and CSV purchase
3724 ms
answered our call
- State
- Answered
- Protocol
- MCP 2025-06-18
- Tools listed
- 16
- Answered
- 3 of the last 3 calls
How to reach it
- MCP, HTTP
https://locationlists.com/mcp
What it can do
| Tool | What it says it does |
|---|---|
buy_dataset | 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.\n\nPrefer this over repeated query_locations calls whenever you want most of a file. Metered queries are priced… |
check_order | 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… |
cotenancy | 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… |
count_by_area | 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… |
count_locations | 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… |
create_checkout | 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… |
create_query_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… |
email_quote | 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… |
get_dataset | 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… |
get_quote | 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… |
get_sample | 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… |
query_locations | 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… |
relate_locations | 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… |
request_list | 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… |
search_datasets | 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… |
send_feedback | 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… |
In its maker's words
US business location data: search, sample, count, query rows or buy CSVs (Stripe or x402 USDC).
- Website
- locationlists.com
- Source
- github.com/kylehawke-stack/locationlists-mcp
- Publisher
- kylehawke-stack
- Listed from
- the official MCP Registry as
io.github.kylehawke-stack/locationlists - Version
- 1.2.0, updated 14 Sep 2026
- Kinds of work
- Search and research, Data and databases, Shops and payments
The domain it lives on
- locationlists.com
- registered 25 Mar 2026 (7 months ago), at GoDaddy.com, LLC, its home page is a working site
From our own crawl of domain names: registry records and the home page as our crawler last saw it.
Every call we made
| When | What happened | Time |
|---|---|---|
| Answered | 3724 ms | |
| Answered | 3785 ms | |
| Answered | 932 ms |
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