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JavaScript
Remote

datasets_apple_books_facets

Discover Apple Books facet values and term counts using the same filters as search to analyze genres, authors, publishers, narrators, providers, currencies, or discovery sources.

Instructions

Discover Apple Books dataset values. Returns term counts over the same filters as search. Facet values: kind, genres, artist_name, publisher, series_name, narrator, provider, currency, discovery_country, discovery_source, free.

Input Schema

NameRequiredDescriptionDefault
qNoFull-text query over name, artist, series, narrator and description; max 256 characters
freeNoFilter for free titles
kindNoRecord kind: book, audiobook, author, series, audiobook_series
facetYesFacet: kind, genres, artist_name, publisher, series_name, narrator, provider, currency, discovery_country, discovery_source, free
genreNoExact Apple Books genre label, max 128 characters
artistNoExact author or artist name, max 128 characters
seriesNoExact series name, max 128 characters
countryNoExact discovery storefront country, max 128 characters
currencyNoExact currency code, max 128 characters
narratorNoExact audiobook narrator, max 128 characters
providerNoExact audiobook provider, max 128 characters
artist_idNoExact numeric Apple Books artist ID, max 128 characters
publisherNoExact ebook publisher, max 128 characters
min_ratingNoMinimum average rating from 0 through 5
discovery_sourceNoExact crawl source, max 128 characters
min_rating_countNoMinimum rating count, 0 or greater

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.18

TDQS

B3.4/5.0
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 does disclose one important behavioral trait: rather than records, it returns term counts, which is genuinely useful for avoiding misuse of the result. It still omits cardinality limits, whether an empty result is possible, and any ordering/count-truncation behavior.

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?

Two compact sentences front-load the purpose and return behavior, and the facet list is easy to scan. The facet enumeration duplicates the schema enum, slightly reducing waste-free conciseness, but overall the definition is well-structured rather than padded.

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 16 parameters, no annotations, and no output schema, the description is thin. It tells the agent that counts come back but not their shape or how the many filters combine (AND vs OR, interaction with 'facet'). Adequate but leaves real gaps for a tool of this complexity.

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 coverage is 100%, so the enum of facet values and the filter descriptions are already fully documented in the schema. The description repeats the facet list but adds no meaning beyond the enum. Per the baseline rule, 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific resource (Apple Books dataset values) and behavior (returns term counts over the same filters as search). It also references the sibling search tool implicitly via 'same filters as search,' which helps an agent understand the relationship, though it never names the sibling explicitly. This is clear enough to identify the purpose but slightly vague as a verb ('discover').

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

Usage Guidelines3/5

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

The phrase 'same filters as search' implies this tool shares the search tool's filter set, hinting at when it is relevant. However, it never states when to choose this over datasets_apple_books_search or datasets_apple_books_item, nor does it set any exclusions. Usage is only inferred, not instructed.

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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