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JavaScript
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datasets_goodreads_authors_facets

Generate facet counts for Goodreads authors by genre or crawl run ID, with filters for name, genre, query, ratings, and rating averages.

Instructions

Facet Goodreads authors dataset. Returns terms aggregation counts for the Goodreads authors dataset. Facet enum: genres, run_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over name, about and genres, max 256 characters
nameNoExact author name filter, max 128 characters
facetYesFacet enum: genres, run_id
genreNoExact genre filter, max 128 characters
run_idNoExact crawl run-id filter, max 128 characters
min_ratingNoMinimum average rating, 0 through 5
min_ratings_countNoMinimum number of ratings

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "genres",
+  "run_id"
+]
  2. Added

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only says it returns 'terms aggregation counts', but doesn't explain response structure, pagination, sorting, or default behavior (e.g., count of genres vs run_id). It also doesn't mention whether filters like q or genre narrow the aggregation or just the dataset. This is minimal and leaves many behavioral aspects unspecified.

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 with no filler. The core function and facet options are front-loaded. Every word earns its place.

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

Completeness2/5

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

For a facet tool with 7 parameters and no output schema, the description is too sparse. It doesn't explain what the returned aggregation looks like (e.g., mapping of facet values to counts), nor does it clarify how filters interact with the aggregation. Without output schema, it should at least describe the response shape. This is insufficient for an agent to reliably interpret results.

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% — all 7 parameters are described in the input schema, including the facet enum. The description repeats the facet enum ('genres', 'run_id') but adds no extra meaning beyond the schema. Since the schema already documents parameters, the description adds marginal value. Baseline 3 applies.

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 clearly states the tool 'Facet Goodreads authors dataset' and specifies it returns 'terms aggregation counts', which distinguishes it from the sibling 'datasets_goodreads_authors_search' (which returns matching records). However, it doesn't explicitly name the alternative or contrast with it, so it's clear but not fully differentiated.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives like datasets_goodreads_authors_search or datasets_goodreads_books_facets. It doesn't mention that facets are for aggregating counts, not retrieving records, nor does it provide any decision criteria. The agent must infer usage from the name and minimal description.

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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