Find carriers by location, fleet and cargo
Find active FMCSA registrations based in Laredo, Texas, with the reported fleet and cargo you select.
What the output looks like
Five saved rows and six selected columns from the DataScoop import recorded at . This is the import time, not the date each record was updated.
| Legal name | USDOT number | City | State | Power units | Drivers |
|---|---|---|---|---|---|
| INTERCON CARRIERS LC | 621366 | Laredo | TX | 94 | 125 |
| SOLO TRANSPORT INC | 776698 | Laredo | TX | 27 | 11 |
| F & A EXPRESS LLC | 814450 | Laredo | TX | 82 | 70 |
| CIRRUS TRANSPORT INC | 912918 | Laredo | TX | 26 | 24 |
| DYNAMIC LOGISTICS LC | 919459 | Laredo | TX | 36 | 60 |
These were the first five matching preview rows captured on 21 October. They are saved output from the dated import; current results can differ after a refresh.
Check the scope and fields
The FMCSA register includes US and cross-border carriers, private fleets and bus operators. This request selects physical addresses in Laredo, Texas. An address is not a statement of the territory a carrier serves.
- Status, operation, state, city and fleet band use equality filters. Classification and cargo use contains filters because a carrier can report several categories.
- USDOT identifiers, company names, physical locations, fleet counts, cargo and registration fields are included. Phone numbers, emails and company-officer names are excluded.
- Active registration and a reported Authorized For Hire classification do not establish current operating authority, insurance, capacity or suitability. Check an individual carrier's current FMCSA record before relying on those details.
Source: FMCSA Company Census File. Source: Federal Motor Carrier Safety Administration, Motor Carrier Census, via the US DOT open-data portal (data.transportation.gov). Public record; not endorsed by or affiliated with the FMCSA or US DOT. Registration details change; check the FMCSA record before relying on them.
The current request's filters
statusActive
Equality; case-insensitivestateTX
Equality; case-insensitivecityLaredo
Equality; case-insensitiveoperationInterstate
Equality; case-insensitivefleetBand21-100 units
Equality; case-insensitiveclassificationAuthorized For Hire
Contains; case-insensitivecargoGeneral Freight
Contains; case-insensitive
strict_filters=1 rejects invalid or unknown nonempty filters. It does not change a contains filter into an exact match.
Use this with an assistant
In an assistant that supports hosted MCP, add https://datascoop.io/mcp using its connection settings. A key is optional for a free preview. Keep any key in the connection settings; do not paste it into a prompt.
Use DataScoop's us-motor-carriers dataset to find active registrations with a physical address in Laredo, Texas, interstate operations and 21–100 power units. Require classification to include Authorized For Hire and cargo to include General Freight. Start with 25 rows. Show legal name, USDOT number, city, state, power units and drivers. Include the source, recorded import date and whether more rows match. Treat these as reported census details, not verified current operating authority or available capacity.
Ask the assistant to inspect get_dataset_schema, then call query_dataset with these arguments:
{
"dataset": "us-motor-carriers",
"filters": {
"status": "Active",
"state": "TX",
"city": "Laredo",
"operation": "Interstate",
"fleetBand": "21-100 units",
"classification": "Authorized For Hire",
"cargo": "General Freight"
},
"strict_filters": true,
"limit": 25,
"offset": 0
}Read total, returned, truncated, applied_filters and provenance.imported_at before describing the result. If more rows match, request further pages only when you need them. A failed query or an empty result is not evidence that no such businesses exist.
Use this in your app
These examples use standard libraries: Node.js 20 or later, or Python 3. Save the file, then run it from a terminal. No account or paid AI model is needed to try a free query. If you have a DataScoop key, set DATASCOOP_API_KEY in your environment; it is sent as a Bearer header.
The code requests 25 rows per page, supports free pagination and stops above 2,500 matching rows or 100 pages. It prints JSON only after all reported rows have been retrieved. A zero-match result prints an empty row list; errors exit without printing a partial list.
JavaScript · Node.js 20+
Save as query.mjs, then run node query.mjs. Download the JavaScript example.
// Node.js 20+. No packages required. Optional key: DATASCOOP_API_KEY.
const endpoint = "https://datascoop.io/v1/datasets/us-motor-carriers/query";
const dataset = "us-motor-carriers";
const filters = {
"status": "Active",
"state": "TX",
"city": "Laredo",
"operation": "Interstate",
"fleetBand": "21-100 units",
"classification": "Authorized For Hire",
"cargo": "General Freight"
};
const PAGE_SIZE = 25, MAX_ROWS = 2500, MAX_PAGES = 100;
const headers = { Accept: "application/json" };
const key = (process.env.DATASCOOP_API_KEY || "").trim();
if (key) headers.Authorization = "Bearer " + key;
async function retrieve() {
const rows = [];
let total = null, importedAt = null, first = null;
for (let page = 0; page < MAX_PAGES; page++) {
const url = new URL(endpoint);
url.search = new URLSearchParams({ ...filters, strict_filters: "1",
limit: String(PAGE_SIZE), offset: String(rows.length) });
const response = await fetch(url, { headers, signal: AbortSignal.timeout(30000) });
if (!response.ok) throw new Error("Query failed: HTTP " + response.status +
". Check filters and access; retry later for rate limits or server errors.");
const result = await response.json();
if (result.dataset !== dataset || !Array.isArray(result.rows) ||
result.rows.some(row => !row || typeof row !== "object" || Array.isArray(row)) ||
!Array.isArray(result.ignored) || result.ignored.length ||
!Number.isSafeInteger(result.total) || result.total < 0 ||
result.offset !== rows.length || result.returned !== result.rows.length ||
result.returned > PAGE_SIZE || typeof result.truncated !== "boolean") {
throw new Error("Unexpected query response; no partial result was printed.");
}
if (result.total > MAX_ROWS) throw new Error("More than " + MAX_ROWS +
" rows match. Narrow the filters or review the bound before increasing it.");
for (const [name, value] of Object.entries(filters)) {
if (String(result.applied_filters?.[name]) !== String(value))
throw new Error("A requested filter was not applied: " + name);
}
const imported = result.provenance?.imported_at;
if (typeof imported !== "string" || !Number.isFinite(Date.parse(imported)))
throw new Error("Import date is missing; no partial result was printed.");
if (first === null) { first = result; total = result.total; importedAt = imported; }
if (result.total !== total || imported !== importedAt)
throw new Error("The import or match count changed. Run the request again.");
const next = rows.length + result.returned;
if (next > total || result.truncated !== (next < total) ||
(result.returned === 0 && next < total))
throw new Error("Pagination stopped making sense; no partial result was printed.");
rows.push(...result.rows);
if (rows.length === total) return { dataset, filters, total, rows,
provenance: first.provenance, licence: first.licence, attribution: first.attribution };
}
throw new Error("Page limit reached. Narrow the filters; no partial result was printed.");
}
try {
const result = await retrieve();
if (result.total === 0) console.error("No matching records. Check the selected area and filters.");
// Keep provenance and attribution with cached or republished data.
console.log(JSON.stringify(result, null, 2));
} catch (error) {
console.error(error instanceof Error ? error.message : "Request failed.");
process.exitCode = 1;
}
Python · standard library
Save as query.py, then run python3 query.py. Download the Python example.
# Python 3. No packages required. Optional key: DATASCOOP_API_KEY.
import json
import os
import sys
from datetime import datetime
from urllib.error import HTTPError, URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen
ENDPOINT = "https://datascoop.io/v1/datasets/us-motor-carriers/query"
DATASET = "us-motor-carriers"
FILTERS = json.loads("{\"status\":\"Active\",\"state\":\"TX\",\"city\":\"Laredo\",\"operation\":\"Interstate\",\"fleetBand\":\"21-100 units\",\"classification\":\"Authorized For Hire\",\"cargo\":\"General Freight\"}")
PAGE_SIZE, MAX_ROWS, MAX_PAGES = 25, 2500, 100
HEADERS = {"Accept": "application/json"}
key = os.environ.get("DATASCOOP_API_KEY", "").strip()
if key:
HEADERS["Authorization"] = "Bearer " + key
def text_value(value):
return "true" if value is True else "false" if value is False else str(value)
def retrieve():
rows, total, imported_at, first = [], None, None, None
for _ in range(MAX_PAGES):
params = dict(FILTERS, strict_filters="1", limit=PAGE_SIZE, offset=len(rows))
request = Request(ENDPOINT + "?" + urlencode(params), headers=HEADERS)
with urlopen(request, timeout=30) as response:
result = json.load(response)
if (not isinstance(result, dict) or result.get("dataset") != DATASET or
not isinstance(result.get("rows"), list) or
any(not isinstance(row, dict) for row in result["rows"]) or
not isinstance(result.get("ignored"), list) or result["ignored"] or
type(result.get("total")) is not int or result["total"] < 0 or
type(result.get("offset")) is not int or result["offset"] != len(rows) or
type(result.get("returned")) is not int or
result.get("returned") != len(result["rows"]) or
result["returned"] > PAGE_SIZE or type(result.get("truncated")) is not bool):
raise ValueError("Unexpected query response; no partial result was printed.")
if result["total"] > MAX_ROWS:
raise ValueError("More than 2500 rows match. Narrow filters or review the bound.")
applied = result.get("applied_filters")
if not isinstance(applied, dict):
raise ValueError("Applied filters are missing.")
for name, value in FILTERS.items():
if name not in applied or text_value(applied[name]) != text_value(value):
raise ValueError("A requested filter was not applied: " + name)
provenance = result.get("provenance")
imported = provenance.get("imported_at") if isinstance(provenance, dict) else None
if not isinstance(imported, str):
raise ValueError("Import date is missing; no partial result was printed.")
datetime.fromisoformat(imported.replace("Z", "+00:00"))
if first is None:
first, total, imported_at = result, result["total"], imported
if result["total"] != total or imported != imported_at:
raise ValueError("The import or match count changed. Run the request again.")
next_offset = len(rows) + result["returned"]
if (next_offset > total or result["truncated"] != (next_offset < total) or
(result["returned"] == 0 and next_offset < total)):
raise ValueError("Pagination stopped making sense; no partial result was printed.")
rows.extend(result["rows"])
if len(rows) == total:
return dict(dataset=DATASET, filters=FILTERS, total=total, rows=rows,
provenance=first["provenance"], licence=first.get("licence"),
attribution=first.get("attribution"))
raise ValueError("Page limit reached. Narrow filters; no partial result was printed.")
try:
result = retrieve()
if result["total"] == 0:
print("No matching records. Check the selected area and filters.", file=sys.stderr)
# Keep provenance and attribution with cached or republished data.
print(json.dumps(result, indent=2))
except HTTPError as error:
print("Query failed: HTTP " + str(error.code) + ". Check filters and access; retry later for rate limits or server errors.", file=sys.stderr)
sys.exit(1)
except (URLError, TimeoutError, ValueError, TypeError, KeyError) as error:
print(str(error), file=sys.stderr)
sys.exit(1)
The code stops if the reported import date or total changes between pages. Requests do not pin a retained snapshot, so an unchanged date does not guarantee that an import cannot cause missing or repeated rows across pages. Keep the source, filters, import date and attribution with saved results; cache them instead of polling.
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