Pre-built CSV location data across 1086 datasets and 15,213,879 business locations — manufacturer dealer networks, state-licensed trade contractors, retail and restaurant chains, healthcare practices, and nonprofits from the IRS files. Each dataset page shows real rows, how full every column is, and the date its file was built, before you pay.
| name | city | state | zip | phone | latitude | longitude |
|---|---|---|---|---|---|---|
| ROLLAND R. ROGERS | NORWALK | OH | 44857 | 4196681101 | 41.216499 | -82.577642 |
| MICHAEL G CARLSON | NASHVILLE | TN | 37203 | 6153425900 | 36.149775 | -86.789146 |
| RENATO A ALFONSO | PALM COAST | FL | 32164 | 3864399777 | 29.480607 | -81.217791 |
| PRITHVI NARAYAN | PHILADELPHIA | PA | 19134 | 2154275196 | 39.989223 | -75.108756 |
| MATTHEW J HOERMANN | FREDERICKSBURG | TX | 78624 | 8309970330 | 30.291786 | -98.870915 |
Real rows from US Healthcare Providers — Full NPPES File
15M+
Location Records
1086
Datasets
217
Datasets rebuilt on a schedule
CSV
Instant Download
1086 datasets organized across 16 top-level categories
By number of rows in the file
Every dataset page is built from the file itself, so what you read there is what you download.
The fields listed on a dataset page are the columns in the file's own header row.
Each page shows how much of the file carries every column. Columns under 1% filled are marked as not in this release.
Each page shows the date its file was built. 217 datasets are rebuilt on an automatic schedule; the rest say so.
Download sample rows and see how many rows fall in each ZIP code before you buy.
| name | city | state | zip |
|---|---|---|---|
| ROLLAND R. ROGERS | NORWALK | OH | 44857 |
| MICHAEL G CARLSON | NASHVILLE | TN | 37203 |
| RENATO A ALFONSO | PALM COAST | FL | 32164 |
| PRITHVI NARAYAN | PHILADELPHIA | PA | 19134 |
| MATTHEW J HOERMANN | FREDERICKSBURG | TX | 78624 |
Need more than a CSV? We turn location data into analysis — competitive footprints, site selection, territory design, and custom research.
See where your locations intersect with competitors. Identify white space and underserved markets.
02Site selection that scores potential locations against your success profile.
03Redesign sales territories for balanced workload, coverage, and travel time.
04Bespoke datasets and analysis for an industry, geography, or question your team needs answered.
Each of these is a real dataset in the catalog, and the reason it works is a column in the file.
Prospecting by firm size
US CPA & Accounting Firms120,179 records · $149
Every row says how many CPAs are registered at that address, so you can segment by firm size instead of guessing from a company name.
Channel & field sales
STIHL Dealers10,503 records · $39
Built from the manufacturer's own dealer locator, so a rep planning a territory works from the same list the brand publishes.
Territory design
US Dental Practices112,228 records · $149
One row per physical location rather than per billing registration, so a territory count means places you can visit. The shared-address count is kept in its own column.
Use with AI assistants
Ask for the list you need in plain English. Your assistant finds it, shows you sample rows and the price, and buys only what you approve.
Using ChatGPT or Claude? Tell it to answer from locationlists.com/find — no account, no connector.
No connector needed. Any AI assistant can answer from this site with a web link, no connector, no account: open https://locationlists.com/find with the question as query parameters.
If a question needs filtering, distance, drive time, combining lists or a price, open a /find link: either put the question itself in q=, or build the structured link. The answer does not need an API, a connector or an account.
Simplest, the question itself: https://locationlists.com/find?q=<the question in plain words>
Or set the parts yourself: https://locationlists.com/find?dataset=<slug>&state=<ST>&city=<city>&county=<county>&zip=<zip>&near=<place or zip>&radius=<miles>&drive=<minutes>&where=<column>:<op>:<value>&total=<rows>
Leave out what the question does not need. Use radius for straight-line miles or drive for minutes of driving (5 to 60), not both. To ask about several lists at once, use datasets with two or more names, or category with a kind of business. To compare two lists, add relate=nearest and the other list with b. in front of its parameters. To count by area, use areas=county (or zip, state, metro). To keep only places whose county, ZIP code, metro area or state meets a Census figure, add area_where (for example county:population>1000000); with areas, per=100000 gives counts per 100,000 residents.
Optional: connect for repeat use or wallet payments
No setup needed for a one-off question: open https://locationlists.com/find with the question as query parameters. Connecting is optional.
Copy this and paste it into ChatGPT or Claude:
Read https://locationlists.com/skill.md and follow the instructions to connect LocationLists.Your assistant will walk you through setup and show you prices before you pay.
Add LocationLists to your assistant yourself:
Then ask for the list you need, for example “find dental practices in Austin, Texas”.
Claude Code:
claude mcp add --transport http locationlists https://locationlists.com/mcp
No MCP client? POST https://locationlists.com/api/x402/query returns HTTP 402 with the price, and rows once paid by x402 (USDC on Base). /llms.txt · /skill.md · /openapi.json
Pay by card. No account needed. More setup options
1086 datasets and 15,213,879 business locations, listed on public directories you can open yourself.
x402 Bazaar
Coinbase's machine-readable index of services AI agents can pay for; our paid query endpoint is in it
x402scan
public index of pay-per-use services
Official MCP Registry
the public directory of AI assistant connectors
Glama
connector directory, rated A
Smithery
connector directory
Hugging Face
free sample files
Kaggle
free sample files
Refund policy. If the data has significant quality problems, email us within 7 days of purchase for a full refund. License terms
Take the whole file once, or let an agent pay for just the rows it reads. No subscriptions either way.
Buy the whole file
$9–$19
Under 2,000 records
$29–$49
2,000–15,000 records
$69–$199
15,000+ records
Or pay per record
Query any dataset over 5,000 records and pay only for the rows you ask for, in USDC over HTTP 402 — no card and no account. The row price is derived from that file’s own list price, so a slice is always cheaper than the file and the file is always cheaper than draining it.
100 rows of US Dental Practices = $0.28 · whole file $149
How agent purchasing worksIf the data has significant quality problems, email us within 7 days of purchase for a full refund. Secure checkout powered by Stripe.
Browse 1086 location datasets and download the data you need.