Skip to main content
Glama
Crawlora-org

Crawlora MCP

Official
by Crawlora-org
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

datasets_boxofficemojo_facets

Returns facet counts for the Box Office Mojo dataset—gross band, genre, franchise, year, and more—scoped to your search filters.

Instructions

Facet the Box Office Mojo dataset. Returns terms-aggregation counts for one facet of the Box Office Mojo dataset, scoped to the same filters as search. Facet enum: gross_band, years_active, lifetime_year, franchise_names, brand_names, genre_names, hydrated, is_billion_dollar, in_lifetime_top_1000_ww. gross_band enum: under_50m, 50_100m, 100_250m, 250_500m, 500m_1b, over_1b.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query, max 256 characters
yearNoYear in years_active
brandNoBrand name filter, max 128 characters
facetYesFacet enum: gross_band, years_active, lifetime_year, franchise_names, brand_names, genre_names, hydrated, is_billion_dollar, in_lifetime_top_1000_ww
genreNoGenre name filter, max 128 characters
hydratedNoHydrated filter
title_idNoExact title id (IMDb tt… id used by Box Office Mojo), max 32 characters
franchiseNoFranchise name filter, max 128 characters
gross_bandNoGross band filter
min_domesticNoMinimum lifetime domestic gross
lifetime_yearNoPrimary lifetime chart year
max_worldwideNoMaximum lifetime worldwide gross
min_worldwideNoMinimum lifetime worldwide gross
is_billion_dollarNoOnly titles with worldwide gross of at least $1B
min_foreign_shareNoMinimum foreign share of worldwide gross, 0 through 1
max_domestic_shareNoMaximum domestic share of worldwide gross, 0 through 1
in_lifetime_top_1000NoOnly titles in the lifetime worldwide top 1000 chart

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "gross_band",
+  "years_active",
+  "lifetime_year",
+  "franchise_names",
+  "brand_names",
+  "genre_names",
+  "hydrated",
+  "is_billion_dollar",
+  "in_lifetime_top_1000_ww"
+]
  2. Added

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden, and it does disclose the key behavioral trait: the tool returns terms-aggregation counts for exactly one facet rather than full records. The read-only nature is implicit in 'returns', though side effects, auth, and rate limits are not mentioned.

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

Conciseness3/5

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

The first sentence, 'Facet the Box Office Mojo dataset', is redundant with the tool name and the following sentence, and the facet enum list largely duplicates the schema. The description is not bloated overall and does add the gross_band enum, but it could be tightened.

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 17-parameter tool with no output schema or annotations, the description explains the return type and that filters match search, which is helpful. However, it does not describe the exact response shape of the terms aggregation (e.g., bucket key/doc_count structure) or constraints on combining multiple filters, leaving some ambiguity for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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, but the description adds real value by enumerating the gross_band values ('under_50m', '50_100m', etc.), which the schema's gross_band parameter lacks. The facet enum list repeats schema content but helps confirm the valid choices.

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 identifies an aggregation endpoint: it 'Facet[s] the Box Office Mojo dataset' and 'returns terms-aggregation counts for one facet'. It also lists the supported facet enum, and the phrase 'scoped to the same filters as search' distinguishes it from the search endpoint, though no sibling is named explicitly.

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?

It gives clear context for when to use the tool: when the user needs facet/aggregation counts over the Box Office Mojo dataset, using the same filters available to search. It does not explicitly state when not to use it or name alternatives like datasets_boxofficemojo_search or datasets_boxofficemojo_item, so it stops short of full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools