particle_podcast_find_mentions
Find dialogue lines where a specific person or company is named in podcast transcripts.
Three response modes
format="summary" (default, wide scan). Returns up to limit episodes (reverse-chronological), each with metadata + the first 10 mention-only lines (just the lines naming the entity, no surrounding dialogue). Use this to see what's been said across episodes and decide which episodes are worth reading in full. Paginate older episodes with cursor.
format="detail" (narrow drill-in). Requires episode_slug. Returns the full mention windows with context_lines of surrounding dialogue around each mention. Pass one slug for a single episode, or up to 10 comma-separated slugs (e.g. episode_slug="all-in-200,all-in-201,all-in-202") to multi-get several episodes in one call. limit/cursor don't apply.
format="compact" (screening). The same episodes as summary, each with its mention count, the strings it was mentioned as, and the segments carrying the mentions (id, title, type, first mention time) — no dialogue lines at all, the smallest shape. limit and cursor page it exactly as summary. Use it to fan out over many entities or a long date range and decide where to read; the segment ids are citations, and format="detail" with the episode slug reads the lines.
Workflow
Two patterns, depending on what you already know:
No specific episode in mind: call
format="summary"first to scan, then callformat="detail"with the slug(s) of the episodes worth reading in full. For most questions (sentiment, recurring themes, who said what when), summary alone has enough signal and the second call isn't needed.Already have the episode slug (e.g. user mentioned the episode by name, or you have it from another tool like
particle_podcast_get_episodeorparticle_podcast_search_transcripts): skip summary entirely and callformat="detail"withepisode_slugdirectly.
Examples
Wide scan, then drill in: User asks "what has All-In said about OpenAI recently?". Call format="summary", company_slug="openai", podcast_slug="all-in", since="2025-11-01", limit=20. Read the mention lines per episode; if 2-3 episodes have substantive discussion, call format="detail", episode_slug="slug1,slug2,slug3" for full context in one round-trip.
Direct drill-in: User says "In All-In #200 they discuss OpenAI's strategy — pull the full quotes". Call format="detail", episode_slug="all-in-200", company_slug="openai" directly — no summary needed.
Who said it
The guest_ filters keep only the mentions a matching guest spoke: guest_field="banking-finance-and-investment" with guest_standing="established" reads what established finance experts said about a company. Codes come from particle_expertise_resolve; for the split of all mentions across occupations or fields use particle_podcast_get_share_of_voice.
When NOT to use this tool
For dialogue that discusses a topic without naming a specific person or company (paraphrase-tolerant search), use particle_podcast_search_transcripts instead — that one ranks segments by relevance to a free-text query.
Required inputs
One of person_slug, company_slug, or entity_slug is required: person_slug for a person, company_slug for a company, entity_slug for any other knowledge-graph entity (places, organizations, events, concepts). Resolve a name to a slug first with particle_person_resolve, particle_company_resolve, or particle_entity_resolve. Slugs are case-insensitive on input.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Constrain how the entity participates: guest, host, panelist, correspondent, or mention. | |
| limit | No | Episodes per page (1-50, default 10). Summary and compact modes — detail returns one episode regardless. | |
| since | No | Only episodes published on or after this ISO 8601 date (e.g. 2025-01-01). | |
| until | No | Only episodes published on or before this ISO 8601 date. | |
| cursor | No | Opaque pagination cursor from a previous summary or compact response's cursor field. Summary and compact modes. | |
| format | No | Response shape. 'summary' (default) returns many episodes (reverse chron) with metadata plus the first few mention-only lines per episode — use this to scan and pick episodes to drill into. 'detail' requires episode_slug and returns one episode's full mention windows with surrounding dialogue context. 'compact' returns the same episodes as summary with the mention count and the segments carrying the mentions (id, title, type, first mention time) and no dialogue — the smallest shape, for screening many entities before reading any. | |
| language | No | Restrict to episodes of podcasts in this language — ISO 639-1 code (e.g. 'fr'). Matches the podcast's primary language subtag, so 'fr' covers 'fr-FR'. | |
| entity_slug | No | Knowledge-graph entity slug from particle_entity_resolve for the long tail that isn't a person or company — places, organizations, events, products, concepts (e.g. 'germany'). Use person_slug for people and company_slug for companies. | |
| guest_field | No | Only mentions spoken by a guest listed under this ANZSRC 2020 field of research: a code, slug or title from particle_expertise_resolve. | |
| person_slug | No | Person slug or encoded person ID from particle_person_resolve, particle_entity_resolve, or the guest tools (e.g. 'sam-altman'). One of person_slug, company_slug, or entity_slug is required. | |
| company_slug | No | Company slug, domain, or canonical ID (e.g. 'nvidia' or 'nvidia.com'). Resolves to the company's linked entity. | |
| episode_slug | No | Episode slug(s) or canonical ID(s). For format='detail', required: pass one slug for a single drill-in or up to 10 comma-separated slugs (e.g. 'all-in-200,all-in-201,all-in-202') for a multi-episode drill-in in one call. For format='summary' or 'compact', optional filter to one episode. | |
| podcast_slug | No | Restrict mentions to a single podcast by slug, internal ID, or numeric iTunes ID. A particle.pro or Radar show link also works. | |
| context_lines | No | Surrounding dialogue lines around each mention (1-20, default 2). Detail mode only — ignored in summary and compact. | |
| output_format | No | Output serialization. 'markdown' (default) returns the LLM-facing rendering. 'json' returns the structured payload as JSON text — use only for programmatic chaining where exact field extraction matters; the JSON shape is larger and noisier for an LLM to read. | |
| guest_in_field | No | Only mentions spoken by a guest speaking within their own field (the guest the other guest_ filters select, when given). | |
| guest_standing | No | Only mentions spoken by a guest whose standing is recognized this way: 'established' (conferred by others), 'self_described', or 'unverified'. | |
| guest_seniority | No | Only mentions spoken by a guest at this seniority or above. | |
| guest_occupation | No | Only mentions spoken by a guest listed under this occupation: a SOC 2018 or ISCO-08 code, slug or title from particle_expertise_resolve (e.g. 'financial-and-investment-analysts'). | |
| guest_practicing | No | Only mentions spoken by a guest who still works in guest_occupation, which must be a detailed SOC 2018 occupation. |