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Enrichment and related workflows can consume credits and may take time. Use the returned schema and quote rather than assuming fields or costs.

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For AI agents

  • Call get_dataset_fields before choosing fields.
  • Preview the dataset and show the expected quote before paid work.
  • Use confirm: true only after explicit approval for quote-gated operations.
  • Preserve workflow_run_id and use get_workflow_run until terminal status.

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Enrich a dataset

Use enrich_dataset after approval. qualify_dataset and research_dataset follow the same quote-then-confirm pattern. transform_dataset changes the shape or filters rows; run_workflow starts a generic workflow. Poll all asynchronous work with get_workflow_run.

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Enrichment workflow

Enrichment normally operates on a matched dataset or another dataset whose fields provide a reliable company or person anchor. First call get_dataset_fields, then request the fields using the names returned for that dataset. Do not assume that a field exists across dataset types. The first enrich_dataset call should omit confirmation when the operation is quote-gated. Show the returned row count, fields, and credit estimate. Only repeat the same request with confirm: true after explicit user approval.
After approval:
Use the returned workflow_run_id to poll. When the run succeeds, continue with its output dataset ID rather than assuming the source dataset was updated in place.
  • qualify_dataset scores rows against explicit criteria and may consume credits.
  • research_dataset performs a quoted research task against supplied context fields.
  • transform_dataset filters or reshapes data and is the read-only way to limit rows before paid work.
  • run_workflow is the generic entry point when a specialized tool does not cover the operation.