List a publisher's podcasts with their suitability verdicts
Returns a paginated list of the publisher’s analyzed podcasts decorated with their latest IAB/GARM tier, confidence, evaluated-at timestamp, and any non-NONE category exposures (flagged categories). Filter by tier(s), by a specific GARM category (optionally constrained to a minimum prevalence and a particular treatment), and choose how to sort (most-risky first by default). Per-category reasoning and evidence excerpts are intentionally omitted — fetch GET /v1/podcasts//suitability for full per-category detail.
Documentation Index
Fetch the complete documentation index at: https://docs.particle.pro/llms.txt
Use this file to discover all available pages before exploring further.
Authorizations
Pass your API key in the X-API-Key header (recommended).
Path Parameters
Podcast publisher slug or ID.
Query Parameters
Results per page
1 <= x <= 100Opaque pagination cursor from previous response
Filter to podcasts whose latest verdict is in this tier set. To OR-filter multiple tiers, pass a comma-separated list (e.g. ?tier=UNSAFE,SENSITIVE).
SAFE, LIMITED, SENSITIVE, UNSAFE Filter to podcasts whose latest assessment shows non-NONE exposure in this GARM category.
adult_sexual, arms_ammunition, crime_harmful_acts, death_injury_military_conflict, online_piracy, hate_speech_aggression, obscenity_profanity, illegal_drugs_alcohol_tobacco, spam_harmful, terrorism, debated_social_issues, misinformation When combined with category, filter to podcasts whose prevalence in that category is at or above this threshold. Defaults to INCIDENTAL — any non-NONE prevalence. Ignored without category.
INCIDENTAL, OCCASIONAL, FREQUENT, PERVASIVE When combined with category, filter to podcasts whose treatment in that category equals this value. Ignored without category.
DOCUMENTARY, EDITORIAL, PROMOTIONAL, GLAMORIZING Sort order. risk_desc ranks the most-risky first (UNSAFE → SAFE); risk_asc inverts that; recently_evaluated sorts by most-recent assessment first.
risk_desc, risk_asc, recently_evaluated