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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds nothing beyond that — no mention of pagination limits (page x page_size <= 10000, page_size max 100) or what a facet response contains, despite these being non-obvious behaviors.

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?

A single clean sentence, front-loaded with the operation and dataset, with no filler. It is efficient, though its brevity is partly under-specification rather than disciplined conciseness.

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?

For a 20-parameter aggregation tool with an output schema, one sentence is thin: it never explains what a facet result is, how facets relate to the filter parameters, or the required 'facet' enum meaning. The rich schema and output schema compensate somewhat, but the description is incomplete for the tool's complexity.

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 description coverage is 100%, with all 20 parameters individually documented including enums, limits, and defaults, so the schema does the heavy lifting. The description adds no parameter meaning beyond the schema, which is the baseline-3 case.

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 phrase 'Facet aggregation over the Box Office Mojo dataset' does name a verb (aggregation) and a resource (the dataset), so the general purpose is inferable. However it gives no sense of what facets are aggregated or how results differ from datasets_boxofficemojo_search/item, so an agent cannot confidently distinguish it from its siblings.

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?

There is no statement of when to choose this tool over datasets_boxofficemojo_search or datasets_boxofficemojo_item, and no exclusions or prerequisites. The agent is left to infer usage entirely from the name.

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