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datasets_apple_podcasts_shows_search

Search indexed Apple Podcasts show records by title, artist, genre, country, and sort filters to discover shows and compare popularity or episode counts.

Instructions

Search Apple Podcasts shows dataset. Searches the crawled public Apple Podcasts show catalog stored in a search index. One row per show. Discovered from a country x genre x collection chart grid and a search-term sweep — not a full catalog of every Apple Podcasts show. Sort enum: relevance, popularity, track_count_desc, release_desc, title_asc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over show title and artist name, max 256 characters
pageNoPage number, defaults to 1
sortNoSort enum: relevance, popularity, track_count_desc, release_desc, title_asc
genreNoExact primary-genre filter (e.g. Comedy, True Crime), max 128 characters
run_idNoExact crawl run-id filter, max 128 characters
countryNoExact storefront country filter (the crawl's discovery storefront, e.g. us, gb), max 128 characters
genre_idNoExact Apple Podcasts genre id filter (e.g. 1303 for Comedy), max 128 characters
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
explicitnessNoExact explicitness filter as reported by Apple (e.g. explicit, cleaned), 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 / sort / enum
      Added value: +[
+  "relevance",
+  "popularity",
+  "track_count_desc",
+  "release_desc",
+  "title_asc"
+]
  2. Added

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the dataset's crawled nature and coverage limitation, but it does not reveal operational behavior such as whether q is required, what result fields look like, or how results are paginated beyond the schema. The limitation disclosure is valuable, yet gaps remain.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact at three sentences, with the core purpose front-loaded and the important coverage caveat following quickly. The opening sentence is somewhat redundant with the tool name, and the sort enum duplication is unnecessary, but overall it is efficient and well-paced.

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?

Given 10 optional parameters with rich schema coverage but no output schema, the description would need to clarify return shape or point to a companion detail tool. It provides a useful coverage caveat but omits typical usage guidance and what a caller receives, leaving the agent to infer from the schema alone.

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 baseline is 3 with no need for the description to compensate. The description's sort enum listing merely repeats what the schema already documents and adds no new meaning about how parameters interact.

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 — 'Search Apple Podcasts shows dataset' — and clarifies it operates on a crawled catalog stored in a search index with one row per show. The provenance detail distinguishes it from a live or full Apple Podcasts search, making the purpose unambiguous even without naming siblings.

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 gives clear context by explaining the dataset was discovered from a chart grid and search-term sweep, and explicitly warns it is not a full catalog of every show. However, it doesn't name alternatives or state when to prefer a sibling tool like apple_podcasts_search or apple_podcasts_show, so the guidance is implicit rather than explicit.

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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