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datasets_boxofficemojo_search

Search and filter theatrical box-office records from Box Office Mojo to find titles by year, genre, franchise, gross band, and worldwide or domestic revenue.

Instructions

Search the Box Office Mojo dataset. Searches theatrical box-office records from Box Office Mojo charts and title pages. Filter by title id, year, franchise/brand/genre, gross band, lifetime top-1000 membership, hydration status, and worldwide/domestic gross ranges. Sort enum: relevance, worldwide_desc, domestic_desc, peak_worldwide_desc, lifetime_rank_asc, year_desc, year_asc. gross_band enum: under_50m, 50_100m, 100_250m, 250_500m, 500m_1b, over_1b. Billing: this search costs 1 credit per record returned, with a minimum of 2 (an empty result costs 2, a full page of 100 costs 100).

Input Schema

NameRequiredDescriptionDefault
qNoFull-text query over title and taxonomy names, max 256 characters
pageNoPage number, defaults to 1
sortNoSort enum: relevance, worldwide_desc, domestic_desc, peak_worldwide_desc, lifetime_rank_asc, year_desc, year_asc
yearNoYear that must appear in years_active
brandNoBrand name filter, max 128 characters
genreNoGenre name filter, max 128 characters
hydratedNoOnly titles with hydrated release groups and market grosses
title_idNoExact title id (IMDb tt… id used by Box Office Mojo), max 32 characters
franchiseNoFranchise name filter, max 128 characters
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
gross_bandNoGross band enum: under_50m, 50_100m, 100_250m, 250_500m, 500m_1b, over_1b
min_domesticNoMinimum lifetime domestic gross in whole USD dollars
lifetime_yearNoPrimary lifetime chart year
max_worldwideNoMaximum lifetime worldwide gross in whole USD dollars
min_worldwideNoMinimum lifetime worldwide gross in whole USD dollars
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. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / gross_band / enum
      Added value: +[
+  "under_50m",
+  "50_100m",
+  "100_250m",
+  "250_500m",
+  "500m_1b",
+  "over_1b"
+]
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "relevance",
+  "worldwide_desc",
+  "domestic_desc",
+  "peak_worldwide_desc",
+  "lifetime_rank_asc",
+  "year_desc",
+  "year_asc"
+]
  2. Added

TDQS

A3.5/5.0
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 behavioral burden. It usefully discloses the credit cost model (1 credit per record, minimum 2, an empty result costs 2, a full page of 100 costs 100), which is real behavioral value, but it says nothing about auth requirements, rate limits, or whether the search is purely read-only.

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 purpose is front-loaded in the first sentence, followed by filter coverage, the two enums, and a clearly separated billing note. It is a bit list-heavy, but each sentence adds usable information and nothing is padded.

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?

For a 19-parameter search tool with no annotations and no output schema, the description covers what is searched, the filterable dimensions, the sort options, and the cost model. Pagination and page-size constraints live in the schema, so the main remaining gap is the lack of return-shape guidance, which is not critical here.

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 description coverage is 100%, so the schema already documents all 19 parameters, and the description largely restates them as a filter list. The enumeration of the sort and gross_band values duplicates the schema enums rather than adding format or interpretation detail beyond them, so 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?

States a specific verb (Search) and resource (the Box Office Mojo dataset of theatrical box-office records from charts and title pages), so an agent can tell what it returns. It doesn't explicitly name or differentiate against siblings like datasets_boxofficemojo_item or datasets_boxofficemojo_facets, leaving that distinction to inference.

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 by the density of filter dimensions (title_id, year, franchise/brand/genre, gross bands, lifetime top-1000, hydration), which signals this is the broad filtering/search entry point. However, there is no explicit when-to-use guidance, no exclusions, and no direction to alternatives such as the item or facets tools.

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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