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particle_podcast_list_clips

Read-only

Browse AI-extracted highlight clips across the catalog, ranked by engagement potential — the shareable moments. Filter by podcast, episode, clip type (FUNNY, CONTROVERSIAL, INSIGHTFUL, ...), minimum engagement score, or speaker — speaker takes a person slug and returns only clips of that person talking ('an insightful Sam Altman clip').

Pass clip_id for one clip's full detail (description, social-hook intro, speaker, audio URL), plus include: ["transcript"] for its dialogue.

For text-based clip discovery — finding clips about a topic or entity — use particle_podcast_search_transcripts instead: matching clips arrive inline on each search result. Episode slugs on every row feed particle_podcast_get_episode.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoClip type filter.
limitNoClips per page (1-50, default 10).
cursorNoOpaque pagination cursor from a previous response.
clip_idNoReturn one clip's full detail instead of a listing. Clip IDs come from this tool, particle_podcast_get_episode with include=clips, and search-result overlapping clips.
includeNoWith clip_id only: 'transcript' attaches the clip's dialogue transcript.
speakerNoRestrict the listing to clips whose primary speaker is this person — a person slug (e.g. 'sam-altman' from particle_person_resolve), a knowledge-graph entity slug for the same person, or an ID.
episode_slugNoRestrict the listing to one episode (slug or ID).
podcast_slugNoRestrict the listing to one podcast (slug, internal ID, or numeric iTunes ID).
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.
min_engagementNoMinimum engagement potential score (0-100). Above 70 is typical for a strong clip.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations only provide readOnlyHint=true; the description adds meaningful behavioral context beyond that, including the engagement-ranked listing behavior, the clip_id detail mode, transcript inclusion semantics, and the practical warning that JSON output is noisier for LLM reading. No annotation contradiction.

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?

Three tight paragraphs front-load the core purpose, then cover the detail mode and the sibling alternative. Every sentence earns its place; no filler or redundant restating of the schema.

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

Completeness4/5

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

For a tool with 10 optional parameters and no output schema, the description covers the main usage modes, filtering dimensions, pagination cursor, and routing to related tools. It could be slightly richer about what the listing rows contain beyond episode slugs, but the schema and provided detail-mode field list make it sufficiently complete.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds genuinely useful semantics: speaker expects a person slug and returns only clips of that person talking, clip_id switches to detail mode, include only applies with clip_id, and output_format has LLM-facing vs programmatic tradeoffs. This goes beyond the schema's brief field descriptions.

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 description names a specific verb and resource — 'Browse AI-extracted highlight clips' — and clearly distinguishes this from text-based discovery via particle_podcast_search_transcripts. It also clarifies the detail-mode variant via clip_id, so an agent can immediately tell what the tool is for.

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 explicitly tells the agent when to use this tool vs alternatives: use particle_podcast_search_transcripts for topic/entity discovery, and use clip_id here for full clip detail. It also routes episode slugs to particle_podcast_get_episode, giving clear selection guidance.

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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