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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).
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. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already give readOnlyHint=true, but the description adds substantial behavioral context beyond that: it explains the ranking signals (salient entities, topics, news story), that each show contributes at most two episodes, republished content is collapsed, and machine-made/syndication feeds are excluded. It also clarifies how same_podcast changes behavior. This is far beyond the annotation and helps the agent set expectations.

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 extremely well-structured: it front-loads the core purpose and ranking logic, then gives usage guidance, exclusions, and parameter hints in a logical flow. Each sentence earns its place; there is no filler or redundancy.

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?

Despite having no output schema, the description tells the agent what to expect (calibrated score and a band) and how to obtain more detail (include:['basis']). It also cross-references related tools for downstream steps (episode slugs feed get_episode, podcast slugs feed resolve), giving a complete picture of how to use the result. For a tool with 7 parameters, this is comprehensive.

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%, so baseline is 3, but the description adds valuable semantics: published_within_days is tied to news-cycle usage, include:['basis'] reveals signals, same_podcast explains exclusion logic, and output_format guidance clarifies when each serialization. These enrich the raw schema descriptions considerably.

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 states precisely what the tool does: returns episodes from other shows covering the same story/subject as a given episode, using a nearest-neighbour search with reranking. It explicitly distinguishes itself from sibling tools (search_transcripts, find_mentions) by naming what it is NOT for, so an agent can immediately select it correctly.

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?

Provides explicit when-to-use ('who else covered this?') and when-not-to-use directives, naming the exact alternatives (particle_podcast_search_transcripts for dialogue, particle_podcast_find_mentions for entity mentions). Also gives parameter-level guidance (published_within_days 7–30, same_podcast semantics) that helps the agent choose the right invocation.

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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