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datasets_apple_podcasts_shows_facets

Facet Apple Podcasts shows data by genre, country, content rating, or crawl run to get term counts, with optional title/artist query and filters.

Instructions

Facet Apple Podcasts shows dataset. Returns terms aggregation counts for the Apple Podcasts shows dataset. Facet enum: genre, genre_id, country, content_advisory_rating, run_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over show title and artist name, max 256 characters
facetYesFacet enum: genre, genre_id, country, content_advisory_rating, run_id
genreNoExact primary-genre filter, max 128 characters
run_idNoExact crawl run-id filter, max 128 characters
countryNoExact storefront country filter, max 128 characters
genre_idNoExact Apple Podcasts genre id filter, max 128 characters
explicitnessNoExact explicitness filter, max 128 characters
min_track_countNoMinimum episode count (track_count), 0 or greater

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "genre",
+  "genre_id",
+  "country",
+  "content_advisory_rating",
+  "run_id"
+]
  2. Added

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that it returns terms aggregation counts, but does not mention read-only nature, pagination, or interaction between filter parameters. This is adequate but not rich; a bit more on response shape or constraints would improve it.

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?

Two sentences, no filler. The core purpose is front-loaded, and the facet enum is listed concisely. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 8 parameters and no output schema, the description should clarify expected response shape and how optional filters interact with the required facet. It states 'returns terms aggregation counts' but does not describe bucket structure or how filters are applied. This is a notable gap for a dataset tool.

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%, so the schema documents all parameters. The description only restates the facet enum, which is already in the schema. It adds no additional meaning about parameter interplay. Baseline 3 is appropriate given high schema coverage.

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 clearly states the tool facets the Apple Podcasts shows dataset and returns terms aggregation counts. It specifies the resource and the operation, and the facet enum is explicitly listed. This distinguishes it from sibling search/item tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage: you call this tool to get facet counts rather than search results. It does not explicitly name alternatives or exclusions, but the purpose is clear enough that an agent would know when to use it for faceting. No bad 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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