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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

TableJSON 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 only cover the safety profile (readOnly, openWorld, non-destructive), yet the description discloses non-obvious behaviors: a per-show cap of two episodes, deduplication of republished content, exclusion of machine-made/syndication feeds, a calibrated score with a strong/moderate/weak band, and provenance fan-out via episode and podcast slugs.

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 definition and organized into definition, usage, and exclusions, with no filler sentences. It is on the long side (three dense paragraphs) but each sentence carries operational information rather than restating the name or schema.

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?

With no output schema, the description still tells the agent what each row carries (score, band, slugs), how to expose the basis signals, and how results are deduplicated and capped, plus how pagination params behave. Nothing needed 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%, so baseline is 3, but the description adds real meaning beyond the schema: published_within_days is framed as 'same news cycle' with a 7–30 recommendation, same_podcast is explained as admitting the show's own episodes (otherwise excluded), and include:['basis'] is tied to the signals returned per row.

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+resource+scope: 'Episodes from OTHER shows that cover the same story or subject as a given episode.' It names the sibling tools it is not (search_transcripts, find_mentions) and explains the ranking basis, so an agent can distinguish it from every related sibling 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?

Explicit when-to-use ('when you already have an episode and want its coverage elsewhere') plus explicit when-not ('Do NOT use it to find dialogue about a topic... nor to find every line naming an entity') with the correct alternative named for each. It also documents the same_podcast and published_within_days switches that change the intended usage.

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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