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particle_podcast_find_mentions

Read-only

Find dialogue lines where a specific person or company is named in podcast transcripts.

Two 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.

Workflow

Two patterns, depending on what you already know:

  • No specific episode in mind: call format="summary" first to scan, then call format="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_episode or particle_podcast_search_transcripts): skip summary entirely and call format="detail" with episode_slug directly.

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.

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

TableJSON Schema
NameRequiredDescriptionDefault
roleNoConstrain how the entity participates: guest, host, panelist, correspondent, or mention.
limitNoEpisodes per page (1-50, default 10). Summary mode only — detail returns one episode regardless.
sinceNoOnly episodes published on or after this ISO 8601 date (e.g. 2025-01-01).
untilNoOnly episodes published on or before this ISO 8601 date.
cursorNoOpaque pagination cursor from a previous summary response's cursor field. Summary mode only.
formatNoResponse 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.
languageNoRestrict 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_slugNoKnowledge-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.
person_slugNoPerson 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_slugNoCompany slug, domain, or canonical ID (e.g. 'nvidia' or 'nvidia.com'). Resolves to the company's linked entity.
episode_slugNoEpisode 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', optional filter to one episode.
podcast_slugNoRestrict mentions to a single podcast by slug, internal ID, or numeric iTunes ID.
context_linesNoSurrounding dialogue lines around each mention (1-20, default 2). Detail mode only — ignored in summary.
output_formatNoOutput 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint annotation, the description discloses concrete behavior: summary returns episode metadata plus the first 10 mention-only lines, detail returns full mention windows with context_lines, limit/cursor apply only to summary, and up to 10 comma-separated episode slugs can be passed. It also explains pagination, output_format trade-offs, and case-insensitive slugs—none of which are visible from the annotation alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long, but every block earns its place: purpose, mode semantics, workflow, examples, exclusion rule, and required inputs are clearly separated with headers. The core purpose and the most important distinction (summary vs detail) are front-loaded before the examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 14-parameter tool with no output schema and only a readOnly annotation, the description is remarkably complete. It defines response shapes for both modes, explains when each mode is appropriate, states required inputs and slug-resolution prerequisites, covers pagination and multi-episode drill-in, and documents the sibling to use instead. Nothing essential for correct invocation or interpretation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds substantial meaning on top: the format parameter selects two very different response shapes, episode_slug supports comma-separated multi-get in detail mode, limit/cursor are summary-only, context_lines is detail-only, and one of person_slug/company_slug/entity_slug must be resolved first. This goes well beyond the baseline expected when the schema already documents each field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence names the verb and resource precisely: 'Find dialogue lines where a specific person or company is named in podcast transcripts.' The two response modes and the explicit 'When NOT to use' section differentiate it from the paraphrase-tolerant search sibling, so an agent can distinguish it from particle_podcast_search_transcripts without inspecting schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a full workflow: use summary first when no episode is known, then drill in with detail; skip summary when an episode slug is already available from tools like particle_podcast_get_episode. It also names the alternative for topic-level conversations ('use particle_podcast_search_transcripts instead'), making the selection rule explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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