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Correct and structure messy address strings. Fixes typos, expands abbreviations (st → Street, ave → Avenue), and infers missing fields like postcode, region, or country. Use this as a pre-step before geocoding when your input data is dirty; pass clean addresses straight to geocode or batch_geocode.

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Example prompts

  • “Clean up these 30 customer addresses before I geocode them”
  • “What’s the proper formatted version of ‘10 downing st london uk’?”
  • “Parse and standardise this list of addresses, then tell me which ones are in the UK”
  • “Fix the typos in these addresses and break them into structured fields”

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What you get back

An array of results aligned with the input addresses. Each entry contains:
  • corrected — the standardised, fully formatted address.
  • components — structured fields (house_number, unit, road, neighbourhood, locality, county, region, postcode, country, country_code). Only populated fields are included.
  • corrections — itemised list of changes made, each with field, original (or null if inferred), corrected, and type (one of typo, abbreviation, inferred, reformatted, removed). Omitted entirely when the input was already clean.
  • confidence — score between 0 and 1. Omitted when 1.0; present when the parser was uncertain.
When every row resolves to the same country (or region), those fields are hoisted to a top-level context object instead of being repeated on each row. Mixed-country batches keep the per-row admin fields.

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When to use this vs geocode

  • Your input is clean (real addresses, complete and well-formed) — skip parse_address and call geocode / batch_geocode directly.
  • Your input is messy (typos, abbreviations, missing fields, inconsistent capitalisation) — call parse_address first, then geocode the corrected string. Cleaner input gives better geocoding hit rates and lets you flag low-confidence rows before spending geocoding budget on them.

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Limits

Up to 100 addresses per call.