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datasets_apple_podcasts_shows_facets

Count Apple Podcasts show values by genre, country, content rating, or run ID with query and filters applied, returning the top 50 facet totals.

Instructions

Facet Apple Podcasts shows dataset. Returns value counts for one field of the Apple Podcasts shows dataset, chosen by the required facet: genre, genre_id, country, content_advisory_rating or run_id. The counts honour the search filters (q, genre, country, min_track_count and others) and cover the top 50 values.

Input 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

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does disclose a meaningful trait beyond the schema: results are capped at the top 50 values, and counts are computed under the active filters. It omits return shape/pagination details and any auth or rate-limit notes, but the top-50 limit is the key behavioral fact an agent needs.

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?

Three tight sentences with the core purpose front-loaded, followed by the required parameter and the filter/scoping behavior. No filler or redundancy.

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

Completeness4/5

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

For a single-purpose factet/aggregation tool with a fully covered schema and no output schema, the description says what is counted, what controls it, and how many results come back. It is nearly self-sufficient; only the exact response structure is left implicit.

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 parameters are fully documented in the schema and the baseline is 3. The description restates the `facet` enum values and gestures at the filter parameters, adding little beyond what the schema already provides.

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 and resource ('Facet Apple Podcasts shows dataset. Returns value counts for one field') and names the exact field set via the required `facet`. It is clearly distinguishable from the sibling datasets_apple_podcasts_shows_search, which would return records rather than value counts.

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?

It establishes the operating context well: the counts honour the same search filters (`q`, `genre`, `country`, `min_track_count`), implying it's used alongside or after a search. However, it never explicitly states when to choose this over the search sibling or any precondition/exclusion.

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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