Skip to main content
Glama

particle-pro

by pro.particle
OverviewDiscussionsAdmin

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). A particle.pro or Radar episode link also works.
podcast_slugNoRestrict the listing to one podcast (slug, internal ID, or numeric iTunes ID). A particle.pro or Radar show link also works.
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. Changed2 schema fields changed
    • changedInput schema / properties / episode_slug / description
      Previous value: -"Restrict the listing to one episode (slug or ID)."New value: +"Restrict the listing to one episode (slug or ID). A particle.pro or Radar episode link also works."
    • changedInput schema / properties / podcast_slug / description
      Previous value: -"Restrict the listing to one podcast (slug, internal ID, or numeric iTunes ID)."New value: +"Restrict the listing to one podcast (slug, internal ID, or numeric iTunes ID). A particle.pro or Radar show link also works."
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, destructiveHint=false, openWorldHint), so the description goes further by explaining ranking behavior, the detail-vs-list mode switch, and output_format trade-offs (JSON 'larger and noisier for an LLM'). It stops short of describing pagination semantics beyond the cursor parameter.

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

Conciseness4/5

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

Three front-loaded paragraphs that each carry distinct information — catalog browsing, detail mode, and the sibling alternative. Slightly long, but no sentence is redundant, and the key routing decision is stated early.

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 10-parameter filter/list tool with annotations and no output schema, the description covers the filter dimensions, the detail mode, the output format choice, and the alternative tool. Nothing an agent needs to call it correctly is missing.

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%, which sets the baseline at 3, but the description adds genuine meaning: speaker takes a person slug and returns only that person talking, clip_id fetches full detail, include=transcript attaches dialogue, and min_engagement above 70 is 'typical for a strong clip'.

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?

States a specific verb and resource ('Browse AI-extracted highlight clips across the catalog, ranked by engagement potential') and names the enumeration of filter dimensions. It clearly distinguishes itself from the transcript-search sibling, so an agent can tell them apart without opening a schema.

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?

Explicitly routes the agent: text/topic-based discovery should use particle_podcast_search_transcripts instead, and clip_id switches the tool into single-clip detail mode. It also names the handoff to particle_podcast_get_episode via episode slugs.

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

All particle-pro toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources