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Particle Podcast List Related Episodes

particle_podcast_list_related_episodes
Read-only

Episodes from OTHER shows that cover the same story or subject as a given episode, best first — a live nearest-neighbour search over episode content, reranked on shared salient entities, shared topics and a shared news story. Each row carries a calibrated score and a band (strong / moderate / weak) to branch on; pass include: ["basis"] to see the signals behind every match. Each show contributes at most two episodes, the same content republished on another feed is collapsed to one row, and feeds the screens flag as machine-made or syndication spam are excluded.

Use it when you already have an episode and want its coverage elsewhere ('who else covered this?'). Add published_within_days (7–30) to keep to the same news cycle; same_podcast: true admits the show's own episodes, which are otherwise excluded.

Do NOT use it to find dialogue about a topic — that is particle_podcast_search_transcripts — nor to find every line naming an entity, which is particle_podcast_find_mentions. Episode slugs on every row feed particle_podcast_get_episode; podcast slugs feed particle_podcast_resolve.

Input Schema

NameRequiredDescriptionDefault
limitNoResults per page (1-50, default 10).
cursorNoOpaque pagination cursor from a previous response.
includeNo'basis' attaches, per result, the signals behind the match: content similarity, shared entities with names, shared topics, a shared news story, shared guests, days apart.
episode_slugYesEpisode slug or ID (from particle_podcast_list_episodes, particle_podcast_get_episode, or a search result). A particle.pro or Radar episode link also works.
same_podcastNoAdmit episodes of the same show. Off by default — a show's own episodes are its episode list (particle_podcast_list_episodes), not its related content.
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.
published_within_daysNoOnly episodes published within this many days of the query episode, on either side. Omit for no window. Use 7–30 for 'who else covered this story'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / episode_slug / description
      Previous value: -"Episode slug or ID (from particle_podcast_list_episodes, particle_podcast_get_episode, or a search result)."New value: +"Episode slug or ID (from particle_podcast_list_episodes, particle_podcast_get_episode, or a search result). A particle.pro or Radar episode link also works."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/non-destructive, and the description goes well beyond them: it discloses the retrieval mechanism (live nearest-neighbour over episode content, reranked on entities/topics/news story), the per-show cap of two episodes, deduplication of republished content, and exclusion of machine-made or syndication-spam feeds. It also explains the score/band output and the `basis` opt-in.

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?

Front-loaded with the core behavior, then usage, then exclusions and slug-routing. Each sentence carries information, though the parenthetical scoring detail and the slug-chaining sentence in the last paragraph make it denser than strictly necessary.

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 7-parameter, no-output-schema search tool, the description covers what is returned (score + band, optional basis), default behaviors (same_podcast off, dedupe, feed exclusions), and how result slugs chain into get_episode and resolve. Nothing needed to invoke 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%, so the baseline is 3, but the description adds genuine semantics the schema lacks: the '7–30' window heuristic for `published_within_days`, the rationale for `same_podcast` being off by default, and the purpose of `include: ["basis"]`. It does not re-explain `limit`/`cursor`/`output_format`, which the schema already covers well.

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?

Opens with a specific verb and resource — 'Episodes from OTHER shows that cover the same story or subject as a given episode' — and immediately states the ranking basis. It also names the sibling it is not (search_transcripts, find_mentions), so an agent can distinguish it without opening any 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?

Gives an explicit trigger ('when you already have an episode and want its coverage elsewhere'), concrete parameter guidance for the news-cycle case, and two named exclusions with the alternative tools to use instead. Both when-to-use and when-not-to-use are covered.

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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