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. Beyond that, the description discloses nothing about behavior, such as what a facet aggregation returns, how filters combine with the facet dimension, or pagination limits (which only appear in the schema).

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, front-loaded sentence with no wasted words. It is efficient, though its extreme brevity borders on under-specification for a 29-parameter aggregation tool.

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's complexity (29 parameters, a required facet selector, output schema present), the description is too thin. It omits what facets are available in the response, when to prefer this over search, and how the numerous filters scope the aggregation.

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%, so all 29 parameters (including the required 'facet' enum-like list) are fully documented in the schema. The description adds no additional meaning about the facet parameter or filtering behavior, so the baseline of 3 applies.

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 states a specific verb and resource ('Facet aggregation over the PlayStation games dataset'), which distinguishes it from the many other dataset facet tools by naming the PlayStation dataset. However, it does not differentiate itself from its own siblings (datasets_playstation_games_search, datasets_playstation_games_item) or explain what 'facet aggregation' yields versus a 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?

There is no guidance on when to use facet aggregation instead of the sibling search or item tools, and no conditions or prerequisites are given. The agent is left to infer the use case entirely.

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