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

With no annotations, the description carries the behavioral burden. It states the operation, the required input of 2-5 models by name/slug, and the comparison dimensions, but it does not describe the response format, error behavior, or how invalid/duplicate models are handled.

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?

One compact sentence that leads with the action, states the accepted count and input format, and enumerates comparison dimensions without redundancy.

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 single-parameter read-only comparison tool, the description covers purpose, input arity, accepted identifiers, and the fields included in the comparison. It does not spell out the response shape, but the listed dimensions effectively indicate what the comparison returns.

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?

The input schema already documents the models parameter at 100% coverage, including min/max count and 'names or slugs.' The description repeats this ('2 to 5', 'name or slug') without adding new semantic detail, so it stays at the baseline.

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 names a specific verb ('Compare'), a concrete resource ('robot lawn mowers'), and a clear scope: 2 to 5 models, compared across named attributes. This distinguishes it from sibling tools like get_robot_mower, list_robot_mowers, and recommend_robot_mower.

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 establishes when to use it: when a side-by-side comparison of multiple mowers across specific dimensions is needed. It does not explicitly name alternatives or exclusion conditions, but the comparison focus provides clear context.

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