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

No annotations are provided, so the description carries the full burden. It discloses the output behavior (up to 3 models, ranked by score, with reasons) and the optionality of inputs. However, it doesn't disclose what happens if no model fits the budget/slope constraints, whether the score is computed internally or from a database, or any rate limits or data freshness. The description adds some behavioral context but not comprehensive.

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 a single, well-structured sentence that front-loads the core purpose, then lists inputs and outputs in a natural order. Every clause earns its place: the input specification, the output count, the ranking criterion, and the reason. No wasted words.

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 recommendation tool with 3 simple parameters and no output schema, the description covers the essential inputs and output shape. It doesn't explain edge cases (e.g., no matching models, invalid slope values) or the meaning of the BestRobotMower Score, but these are minor gaps given the tool's simplicity. The description is complete enough for an agent to call it correctly.

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 the schema already documents all three parameters. The description adds context by explaining how the parameters are used (lawn area is required, slope and budget are optional constraints) and that the output is ranked by a score. This is slightly above baseline but doesn't add significant new meaning beyond the schema.

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 ('Recommend'), a resource ('robot lawn mowers'), and the exact inputs (lawn area, slope, budget) and output (up to 3 models ranked by BestRobotMower Score with reasons). This clearly distinguishes it from siblings like list_robot_mowers (which likely lists all) and compare_robot_mowers (which likely compares specific models).

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 implies when to use this tool: when you need recommendations for a specific yard, with area required and slope/budget optional. It doesn't explicitly state when not to use it or name alternatives, but the context signals and sibling names make the use case clear. A small gap: it doesn't explicitly say 'use list_robot_mowers to see all models' or 'use compare_robot_mowers to compare specific models.'

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