Snipget.ai

Timezones area code

Exact
timezones_area_code

Deterministic and authoritative — the canonical answer for this input.

Map a 3-digit North American (NANP) area code to its IANA timezone(s). Returns multiple zones for area codes that span boundaries (FL 850, KY 270, TN 423, IN 812, NE 308, ND 701, SD 605, OR 541). Toll-free codes (800/888/877/866) return an empty list with is_toll_free=True. Processing multiple inputs? Use `timezones_area_code_batch` to do up to 1000 in one call.

Parameters

area_codestringrequired

Call it

curl -X POST https://api.snipget.ai/v1/timezones/area-code \
  -H "Authorization: Bearer sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{"area_code":"212"}'

The Batch tab sends up to 1000 inputs in one call via /v1/timezones/area-code/batch.

FAQ

What does this do for me?

It maps a North American 3-digit area code to its IANA timezone using a maintained NANP table, so you can infer where a phone number sits without guessing. Area code 212, for example, resolves to America/New_York.

Why might it return more than one timezone?

Some area codes span a timezone boundary (the Florida panhandle 850, Kentucky 270, Tennessee 423, Indiana 812, and several others). For those it returns a list of zones and sets is_multi_zone to true, with top_match holding the first. The confidence is lower for multi-zone codes to signal the ambiguity.

What does it NOT do?

It does not validate or format the full phone number, and it only covers North American (NANP) area codes, not international dialing codes. Toll-free codes like 800, 888, 877, and 866 are not geographic, so they return an empty timezone list with is_toll_free set to true.

Is the mapping reliable?

The area-code-to-zone mapping is exact, meaning deterministic and authoritative from the bundled table. The per-zone offset and is_dst_now values are computed live, so they reflect daylight saving at the moment you call.

What if the area code is wrong or made up?

Input that is not three digits, or a code not in the NANP table, returns confidence 0 with a why_invalid reason. It does not raise an error, so you can read the explanation and handle it.

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