Build the smallest useful extraction first
Start with a read-only reporting question, one ad account, a bounded date range, and an explicit output grain. A successful request is only the first test. The returned rows still need to reconcile against the platform interface and preserve the settings that determine what each metric means.
The first implementation in this hub pulls campaign-level Meta Ads Insights into a new Google Sheet with Apps Script. It keeps the token outside the workbook, follows cursor pagination, limits retries, preserves raw action arrays, and refuses to overwrite an existing output tab.
The paid-search material explains how I use Google Ads and Microsoft Advertising APIs for live-state audits, longer-window query reviews, bounded change sets, and post-change verification. It also documents the larger reporting stack: normalized cross-source inputs, explicit freshness, graceful degradation, reversible mutations, experiment logs that can return “no winner,” and one-object checks before a batch expands. Start with how I use APIs in paid search work.
What every advertising API workflow should expose
- the account, endpoint, API version, and permissions used;
- the entity level and time grain of every row;
- the reporting time zone, currency, and attribution settings;
- pagination, retry, and failure behavior;
- the raw values needed to investigate a transformed metric;
- the reconciliation performed against the platform interface;
- the stable entity IDs used to join evidence and apply changes;
- the dry-run, approval, read-back, and assertion required for mutations;
- the owner, schedule, log, and manual fallback.
The future sequence will cover schema changes, cross-platform normalization, credential handling, backfills, asynchronous reports, and when a direct API is unnecessary machinery.
Decide whether the API is necessary machinery
The direct API is not the automatic next step after a spreadsheet. Use the advertising API decision guide to compare a manual export, formula, Apps Script, connector, and direct API against the same decision, frequency, failure cost, control, ownership, and recovery requirements.
Once the workflow can recommend or change live state, apply a consequence-based approval boundary. Separate explanation, recommendation, staged changes, bounded execution, and decisions that must retain accountable human authority.