datasets_boxofficemojo_facets

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

With no annotations, the description carries the full burden. It discloses that it returns terms-aggregation counts and enumerates valid facet values and gross_band values. However, it doesn't describe the output structure (e.g., bucket keys, missing buckets), behavior when filters yield no results, or whether all filter parameters from search are fully supported despite the 'same filters as search' claim.

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

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

The description is very concise at two sentences, front-loading the primary purpose and then enumerating supported facets and gross_band values in a compact, scannable format. Every word earns its place; no fluff or repetition of schema details.

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

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

Given the tool has 17 parameters, no output schema, and no annotations, the description provides the core behavior and key enums but lacks detail on the exact response shape (e.g., list of buckets with counts) and edge cases. The schema descriptions cover parameters, but the description doesn't fully compensate for the missing output schema and annotations.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by listing the facet enum values and the gross_band enum values (which are not in the schema enums but only in descriptions), and by clarifying that filters are scoped like search. This goes beyond the schema's parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Facet the Box Office Mojo dataset. Returns terms-aggregation counts for one facet...' This uses a specific verb ('Returns'), identifies the resource ('Box Office Mojo dataset'), and differentiates from sibling tools like search by focusing on aggregation counts and referencing 'same filters as search'.

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

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

The description provides clear context for when to use the tool by stating it returns facet counts scoped to the same filters as search. This implies usage for analytical aggregation on filtered data, but it doesn't explicitly exclude scenarios like returning individual records or mention alternatives such as the item or search tools.

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