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particle_podcast_list_related

Read-only

List the shows most related to a podcast, best first — "shows like this show". Each result carries the related show's slug, a calibrated score in (0,1], and a coarse band (strong: same beat and audience; moderate: overlapping subject or audience; weak: a loose connection) to branch on. Add include: ["basis"] to see WHY each pair is related: content similarity of recent episodes, shared topics, shared guests (named), same publisher, shared sponsors — use it to explain a recommendation or to keep only pairs related for the reason you care about (shared guests for booking, content for media planning).

Related sets are precomputed per show from its transcripts, topic profile, guest roster, network and advertisers, restricted to the show's language. Only shows above a relatedness floor are listed, machine-generated and farmed feeds are never listed, and a publisher's duplicate feeds of one show appear once. An empty FIRST page is not an error: its coverage says whether the set is not computed yet, nothing cleared the floor, or the request's filters and the default policy removed everything; an empty page reached through a cursor is simply the end of the list.

Not a topic browser: for shows that COVER a topic use particle_podcast_resolve with topic_slug. Not a guest lookup: for where a person has appeared use particle_podcast_get_guest. Not advertiser co-occurrence: use particle_podcast_get_sponsors. Every related show's slug feeds particle_podcast_resolve, particle_podcast_list_episodes and the other podcast tools; person slugs in the basis feed particle_podcast_get_guest, topic slugs feed particle_podcast_resolve's topic_slug. For the five most related shows inline on a resolve, pass include: ["related"] to particle_podcast_resolve instead of calling this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoResults per page (1-50, default 10).
cursorNoOpaque pagination cursor from a previous response.
includeNoOptional response sections. 'basis' attaches, per result, the signals that make the two shows related: content similarity of recent episodes, shared topics, shared guests (named), same publisher, shared sponsors. Off by default; opt in when you need to explain or filter by the reason.
languageNoOnly shows in this language: an ISO 639-1 code such as 'en' or 'es'.
min_scoreNoDrop results below this fused score (0-1]. Prefer branching on each result's band (strong / moderate / weak); score thresholds may be recalibrated as the ranker improves.
podcast_slugNoThe source podcast — slug (e.g. 'all-in' from particle_podcast_resolve), 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_popularityNoOnly shows at or above this popularity percentile (0-1]. A floor above 0 excludes non-charting shows; 0 applies no floor.
suitability_tierNoOnly shows whose latest brand-suitability tier is this value; never-assessed shows are excluded.
publishing_statusNoOnly shows that released an episode in the last 90 days ('active') or did not ('dormant'); shows with no known episode date match neither.
exclude_same_publisherNoDrop shows from the source show's own publisher.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses key behaviors: results are precomputed, restricted to a single source podcast, ranked best-first, filtered by relatedness score, language, popularity, suitability, and publisher exclusion. It also discloses edge behavior ('An empty page is not an error') and explains the meaning of score bands and the 'basis' object. The description carries the behavioral transparency burden almost entirely on its own.

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 earned: it front-loads the core purpose in the first sentence, then organizes additional context into a few dense, high-value paragraphs. Every sentence adds operational meaning (empty-page semantics, band meanings, output format guidance, filter behavior) rather than restating schema fields.

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?

Given the tool's complexity (filters, output formats, result semantics, sibling relationships), the description covers the operational surface thoroughly: what results look like, how scores/bands behave, what filters do, when to use JSON vs markdown, and how this tool differs from alternatives. There is no output schema, so this prose carries the full burden and succeeds.

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?

The input schema already covers every parameter with detailed descriptions, so the description needn't duplicate that. It adds useful semantic context beyond the schema: how scores/bands should be interpreted, how include affects output, when JSON output is appropriate, and how objects can be fed from resolve calls. Minor gap: some params like limit/cursor aren't explicitly called out in prose, but the schema covers them 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?

The description opens with a specific verb and resource: 'List the shows most related to a podcast, best first.' It clearly identifies the tool as a show-level related-list (not episodes or guests), and explicitly contrasts it with sibling tools like particle_podcast_resolve, particle_podcast_get_guest, and particle_podcast_list_related_episodes. An agent can distinguish it from alternatives immediately.

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 gives explicit when-to-use and when-not-to-use guidance: it says to use particle_podcast_resolve for topic lookups, get_guest for people, and list_related_episodes for episode-level recommendations. It also explains when to prefer include='basis' or output_format='json'. This is exemplary usage clarity.

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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