particle_podcast_get_episode_timeseries

Time-bucketed episode counts — the purpose-built answer to “how often is X discussed over time”. Counts episodes matching the same filters as particle_podcast_list_episodes (person, company, entity, podcast, keyword, language, duration, transcript availability) per day, week, or month, plus range totals. keyword_search additionally counts matching transcript segments per bucket — exact counts, no pagination floors. Use this for trend charts instead of paging particle_podcast_list_episodes or particle_podcast_find_mentions once per period. Buckets are UTC-aligned, zero-filled, and Monday-aligned for weeks; a published_after/published_before inside a bucket produces a partial first or last bucket labeled with the full bucket’s start date. Omitting published_after aggregates all time. Ranges are capped at 1000 buckets. At least one subject filter is required: podcast_slug, person_slug, company_slug, entity_slug, or keyword_search.

Inputs

Field	Type	Required	Default	Description
`podcast_slug`	string	one subject filter required	—	Podcast slug, internal ID, or numeric iTunes ID.
`person_slug`	string	one subject filter required	—	Person slug or encoded person ID (e.g. `"sam-altman"`). Counts episodes featuring the person as a speaker — and, when the person has a linked knowledge-graph entity, episodes that mention them.
`company_slug`	string	one subject filter required	—	Company slug, domain, or ID. Resolves to the linked entity.
`entity_slug`	string	one subject filter required	—	Knowledge-graph entity slug for the long tail that isn’t a person or company (e.g. `"germany"`).
`keyword_search`	string	one subject filter required	—	Keyword filter over transcript content. Double-quoted substrings must appear as exact phrases; unquoted terms must all appear in one transcript segment. Adds per-bucket mention counts.
`role`	string	no	—	Role filter when a person/company/entity filter is set: `guest`, `host`, `panelist`, `correspondent`, or `mention`.
`published_after`	string	no	all time	Inclusive range start (ISO 8601 date or date-time).
`published_before`	string	no	now	Range end (ISO 8601 date or date-time).
`interval`	string	no	`week`	Bucket width: `day`, `week`, or `month`.
`language`	string	no	—	ISO 639-1 language code.
`has_transcript`	boolean	no	false	Only count episodes with a completed transcript.
`min_duration` / `max_duration`	integer	no	—	Episode duration bounds in seconds.

Output

A markdown document with ## Episode timeseries — per <interval>, **Range:**, **Total episodes:**, and **Distinct podcasts:** rows (plus **Total mentions:** when keyword_search is set), followed by a table of buckets — Bucket | Episodes columns, with a Mentions column added when keyword_search is set. Buckets are contiguous and zero-filled in ascending order. Sample (person_slug="sam-altman", interval="week", truncated):

## Episode timeseries — per week

**Range:** 2026-06-01 → 2026-06-29
**Total episodes:** 12
**Distinct podcasts:** 7

| Bucket | Episodes |
| --- | --- |
| 2026-06-01 | 8 |
| 2026-06-08 | 4 |

Example

Agent calls: particle_podcast_get_episode_timeseries {
  "keyword_search": "\"artificial general intelligence\"",
  "interval": "month",
  "published_after": "2026-01-01"
}

REST equivalent: GET /v1/podcasts/episodes/timeseries.
For the underlying episodes, use particle_podcast_list_episodes; for the dialogue lines themselves, particle_podcast_find_mentions.

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Tools

particle_podcast_get_episode_timeseries

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