Export observation data as a structured dataset. Supports filtering by time, geography, venue type, and observation family.
Queries the relevant table based on the selected dataset type, applies
filters, and returns every matching row as structured data, a page at a time:
up to 10,000 observation rows or 1,000 cross-signal insights per call, newest
first. When more rows match, metadata.truncated is true and
metadata.next_cursor reads the next page: call again with the same dataset and
filters and cursor set to it, until truncated is false.
WHEN TO USE:
- Exporting audience data for external analysis
- Building datasets for machine learning or reporting
- Getting structured vehicle or commerce data for a specific time/place
- Creating cross-signal datasets for correlation analysis
RETURNS:
- data: Array of dataset rows (schema varies by dataset type)
- metadata: { row_count, export_id, dataset, filters_applied, time_range, truncated, next_cursor }
- suggested_next_queries: Related exports or analyses
Dataset types:
- observations: Raw observation stream data (all families)
- audience: Audience-specific data (face_count, demographics, attention, emotion)
- vehicle: Vehicle counting and classification data
- cross_signal: Pre-computed cross-signal correlation insights
EXAMPLE:
User: "Export audience data from retail venues last week"
export_dataset({
dataset: "audience",
filters: {
time_range: { start: "2026-03-09", end: "2026-03-16" },
venue_type: ["retail"]
},
format: "json"
})
User: "Get vehicle data near geohash 9q8yy"
export_dataset({
dataset: "vehicle",
filters: {
time_range: { start: "2026-03-15", end: "2026-03-16" },
geo: "9q8yy"
}
})