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

Beyond the readOnlyHint annotation, the description discloses the underlying calculation (12 rents divided by sale price per median m²) and labels the result as gross annual yield. This gives the agent useful behavioral context, though it does not discuss aggregation caveats or return shape.

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?

Two sentences carry the formula, the metric name, and the use case with no filler. The definition is front-loaded and every clause earns its place.

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?

The description is sufficient for a read-only metric tool: it explains what is computed and for what decision. It could be slightly more complete by explicitly noting the citywide fallback when bairro is omitted, though the schema already documents that behavior.

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 reinforces the bairro-level scope but adds no parameter-specific details beyond what the input schema provides.

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 clearly identifies the metric ('Rendimento bruto anual do aluguel') and the exact formula, and states the practical question it answers: whether it is worth buying to rent in a neighborhood. It does not explicitly contrast with sibling tools like preco_m2 or ranking_bairros, so it stops short of a 5.

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 phrase 'Serve para responder se compensa comprar para alugar num bairro' gives a clear decision-oriented use case. It does not mention when not to use it or name alternatives, but the intended context is reasonably clear.

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