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Particle Podcast List Related

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

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. 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_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. Changed1 schema field changed
    • changedInput schema / properties / podcast_slug / description
      Previous value: -"The source podcast — slug (e.g. 'all-in' from particle_podcast_resolve), internal ID, or numeric iTunes ID."New value: +"The source podcast — slug (e.g. 'all-in' from particle_podcast_resolve), internal ID, or numeric iTunes ID. A particle.pro or Radar show link also works."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare read-only, non-destructive, open-world behavior. The description adds substantial context beyond that: related sets are precomputed per show from transcripts, topics, guests, network, and advertisers; they are restricted to the show's language; only shows above a relatedness floor are listed; machine-generated and farmed feeds are never listed; duplicate publisher feeds appear once; and empty-page semantics (first page vs. cursor page) are explained in detail. This is rich behavioral disclosure.

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?

Despite its length, the description is front-loaded with the core purpose and then structured into distinct, purposeful paragraphs covering results, filtering rules, empty-page behavior, alternatives, and cross-tool chaining. Every sentence adds decision-relevant information for an agent navigating a complex 11-parameter tool with no output 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?

Given 11 parameters, no output schema, and rich annotations, the description is complete for correct invocation: it explains what is returned (slug, score, band, optional basis), how to branch on results, filtering behavior, empty-page semantics, and exact alternatives and downstream tool integrations. Nothing essential for an agent to call this tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for all 11 parameters, so the schema already documents every parameter thoroughly. The description repeats a few parameter hints (e.g., include: ["basis"], preferring band over min_score) but adds no new syntax, format, or constraint details beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 a specific verb (List) and resource (shows most related to a podcast), with a clear scope ('shows like this show'). It explicitly distinguishes from siblings by naming particle_podcast_resolve, particle_podcast_get_guest, and particle_podcast_get_sponsors as alternatives for different needs.

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?

It gives explicit when-to-use scenarios ('use it to explain a recommendation or to keep only pairs related for the reason you care about'), when-not-to-use cases ('Not a topic browser... Not a guest lookup... Not advertiser co-occurrence'), and a direct alternative for a common sub-case ('For the five most related shows inline on a resolve, pass include: ["related"] to particle_podcast_resolve instead of calling this tool'). This is comprehensive routing guidance.

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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