Crawlora MCP

datasets_apple_books_facets

Read-only

Return counts for a selected Apple Books dataset facet, honoring the same filters as datasets_apple_books_search. Facets are kind, genres, artist_name, publisher, series_name, narrator, provider, currency, discovery_country, discovery_source, and free.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "facet": "<facet>"
    },
    "name": "datasets_apple_books_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Optional full-text query over title/name, artist, series, narrator and description, max 256 characters.
`free`	No	Optional free-title filter.
`kind`	No	Optional resource kind. Allowed values: book, audiobook, author, series, audiobook_series.
`page`	No	1-based page number, default 1; page times page_size must not exceed 10000.
`sort`	No	Sort order: rating_count_desc, rating_desc, release_date_desc, name_asc, crawled_at_desc. Defaults to rating_count_desc without q and relevance with q.
`facet`	Yes	Required facet. Allowed values: kind, genres, artist_name, publisher, series_name, narrator, provider, currency, discovery_country, discovery_source, free.
`genre`	No	Optional exact genre label as stored on Apple Books records, max 128 characters. Use the genres facet to discover labels.
`artist`	No	Optional exact artist/author name, max 128 characters.
`series`	No	Optional exact series name, max 128 characters.
`country`	No	Optional discovery storefront country code, max 128 characters.
`currency`	No	Optional exact currency code, max 128 characters.
`narrator`	No	Optional exact audiobook narrator, max 128 characters.
`provider`	No	Optional exact audiobook provider, max 128 characters.
`artist_id`	No	Optional exact numeric Apple Books artist ID, max 128 characters.
`page_size`	No	Page size, default 20, max 100; page times page_size must not exceed 10000.
`publisher`	No	Optional exact publisher, max 128 characters.
`min_rating`	No	Optional minimum average rating from 0 through 5.
`discovery_source`	No	Optional exact crawl discovery source, max 128 characters.
`min_rating_count`	No	Optional minimum number of ratings, 0 or greater.

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 31, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds the meaningful behavioral facts that the output is counts, not records, and that it reuses the search filter semantics, which is the key insight for predicting results. It leaves pagination/return-shape details to the output schema.

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, front-loaded with the action and scope, followed by the facet enumeration. No filler, no redundant restatement of annotations.

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?

With a 19-param schema fully documented and an output schema present, the description covers the pieces the schema cannot: that this is an aggregation tool and that filters mirror the sibling search tool. Minor gap: it doesn't clarify whether counts are top-N, ordered, or paginated across facets, which matters for a 19-param aggregation call.

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% and the facet property already lists all allowed values, so the description largely restates the schema's facet enum. The one genuine addition is that all filters behave identically to datasets_apple_books_search, which helps the agent reason about the 18 filter params without re-reading each one. Baseline 3 applies given the schema already carries the semantic load.

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?

States a specific verb ('Return counts') on a specific resource ('Apple Books dataset facet') and enumerates the exact facet values available. This clearly distinguishes it from datasets_apple_books_search (which returns records) and datasets_apple_books_item without opening either schema.

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?

Points the agent to datasets_apple_books_search by tying filter semantics to it, implying this tool is for aggregations under the same filter set. It doesn't explicitly say 'use this instead of search when you want distribution counts,' so the choose-this-over-that routing is implied rather than stated.

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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