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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the bar is lower. The description adds useful behavioral context beyond annotations: no API key needed, free, instant answers, and a curated data source.

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?

Three concise sentences, front-loaded with the most useful information, with no redundancy. Every sentence contributes distinct value: cost/auth, purpose, and routing to the alternative.

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 one required parameter and no output schema, the description covers the key invocation context: data source, budget relevance, use case, and sibling differentiation. It does not detail response shape, but the stated purpose strongly implies the output.

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 documents all parameters. The description adds little parameter-level meaning beyond mentioning budget and a typical 2-3 result count, which matches the default limit.

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 uses a specific verb 'Recommend' with a clear resource: lower-risk used vehicles ('Tanks') from BuyerAware's curated 47-platform database near a budget. It distinguishes itself from analyze_listing by explicitly contrasting the use case.

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?

It provides explicit when-to-use guidance: 'Use when a buyer asks what to buy instead, or what is reliable for $X.' It also names the alternative: 'Use analyze_listing instead when they have a specific car in mind.'

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