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datasets_goodreads_books_facets

Generate term aggregation counts for Goodreads books by genres, format, language, publisher, author, series, and publication year to filter and analyze datasets.

Instructions

Facet Goodreads books dataset. Returns terms aggregation counts for the Goodreads books dataset. Facet enum: genres, format, language, publisher, primary_author, primary_author_id, series_name, publication_year, run_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over title, author and description, max 256 characters
isbnNoExact ISBN-10 filter, max 128 characters
facetYesFacet enum: genres, format, language, publisher, primary_author, primary_author_id, series_name, publication_year, run_id
genreNoExact genre filter, max 128 characters
authorNoExact author name filter, max 128 characters
formatNoExact format filter, max 128 characters
isbn13NoExact ISBN-13 filter, max 128 characters
run_idNoExact crawl run-id filter, max 128 characters
seriesNoExact series name filter, max 128 characters
languageNoExact language filter, max 128 characters
author_idNoExact Goodreads author id filter, max 128 characters
max_pagesNoMaximum page count
min_pagesNoMinimum page count
publisherNoExact publisher filter, max 128 characters
min_ratingNoMinimum average rating, 0 through 5
min_ratings_countNoMinimum number of ratings
max_publication_yearNoMaximum publication year
min_publication_yearNoMinimum publication year

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "genres",
+  "format",
+  "language",
+  "publisher",
+  "primary_author",
+  "primary_author_id",
+  "series_name",
+  "publication_year",
+  "run_id"
+]
  2. Added

TDQS

B3.4/5.0
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 does disclose the core behavior (returning terms aggregation counts), but it omits details like whether counts are limited to top terms, whether all filters combine with AND semantics, or any pagination/counting limits. Adequate for a read-only facet tool but not deeply transparent.

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 and front-loaded with the purpose, with the enum list in a single line. It earns most of its place, though 'Goodreads books dataset' is repeated twice and the first sentence is nearly redundant with the tool name.

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?

For a single-required-parameter tool it is enough to make a first call correctly, but with no output schema and no annotations the agent is left without guidance on the result shape (e.g., bucket keys and counts) or how filter parameters narrow the counts. A sentence about the response format would close the gap.

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?

The schema documents all 18 parameters with 100% coverage, so the description adds no new meaning beyond what the schema already provides. Listing the facet enum in the description duplicates the schema's enum rather than enriching it.

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 action ('Facet... Returns terms aggregation counts') on a specific resource (Goodreads books dataset) and enumerates the valid facet fields. This clearly distinguishes it from the sibling search tool datasets_goodreads_books_search, though it does not explicitly name that alternative.

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?

Usage is implied: call with a facet enum to receive counts, and use the filter parameters to narrow the dataset. However, there is no explicit guidance on when to prefer this over the search tool, or how filters interact with the aggregation, so the agent must infer the intended usage context.

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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