insights/services/mcp/definitions/prompts/query-trends-actors.md at main

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zeekay 1b367e2b62 chore: rebase the tree onto upstream master

The tree was a debranded snapshot of upstream taken around 2026-02-20, and
upstream has moved about five months since. This replaces it with upstream
master, mechanically re-derived: bin/debrand does the rename, and the
pipeline and squashed migrations are restored from the shipped tree.

Not a merge. The previous import replaced the tree wholesale in a single
commit, so there is no shared content history to merge against. Reproducing
the rename on the new tree is what actually converges.

What survives: .hanzo/workflows, the Dockerfiles and bin/docker* entrypoints,
compose files, LLM.md, and insights/migrations. The squashed initial
migration is load-bearing — the live database records it by name, so
upstream's ~918 individual migrations cannot come back.

Nine products are dropped as unreferenced. Twenty-one more that we do not run
are kept anyway because the core app still imports them; unpicking those is a
refactor and does not belong in a rebase.

This base does not build yet, and the reasons are not in the rename:
upstream now wants Python 3.13.13 against our 3.12.12 and Django 5.2 against
our 4.2, and the dependency manifests still name upstream packages that we
republish under our own names. Those are decisions, not renames.

2026-08-01 15:44:40 -07:00

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List the persons behind a specific data point in a trends insight. Use this to answer "who were the users that did X on day Y?" or "which users are in this breakdown bucket?".

Pair this with query-trends: first run the trends query to identify the data point of interest, then call this tool with the same trends query as source plus selectors that narrow to one cell.

Selectors:

day (required): a single bucket date as an ISO date string (YYYY-MM-DD), e.g. "2024-01-15". Must match exactly one data point from the trends result.
series: 0-based index of the series to drill into when the trends query has multiple series. Defaults to 0.
breakdown: always an array, one value per breakdownFilter.breakdowns dimension, in the same order. Single dimension: breakdown: ["Opera"]. Multiple dimensions: breakdown: ["Opera", "en-US"].
compare: current (default) or previous when the source has compareFilter enabled.
includeRecordings: defaults to true. Set to false to skip fetching matched session recordings (faster if recordings are not needed).

Response:

Each returned row contains distinct_id, name, email, and event_count (number of matching events for that actor). When includeRecordings is true (the default), a recordings column is also returned containing Insights replay URLs that can be opened in a browser to watch the user's session. Results are limited to the top 100 actors ordered by event count.

Guidance:

Keep the source trends query minimal - only include the filters/breakdowns needed to identify the cell.
Always pick a specific day from the trends result.
For large result sets, tighten the trends query (filters, date range) rather than expecting more rows.

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