Metro Quadrado

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful scoping information such as the 2-6 place limit and the 'Cidade/Bairro' formatting rule, but it does not disclose output shape, limitations, or default behavior beyond what the schema already provides.

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 short, information-dense sentences with no filler. The main action and scope come first, and the input format clarification follows immediately. Every sentence 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?

For a simple read-only comparison tool, the description covers what is compared, how many places can be compared, and how to format each place. The schema covers constraints and defaults. It is slightly incomplete because it does not describe the output shape or provide explicit routing to sibling tools, but an agent can still invoke 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 parameters, enums, and defaults. The description adds concrete format examples ('Joinville' vs 'Jaraguá do Sul/Amizade') and clarifies the comparison criterion, but it does not meaningfully extend parameter semantics beyond the structured 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 uses a concrete verb ('Compara'), specifies the resource (2 a 6 lugares), and defines the comparison metric (preço do m²). It also clarifies the accepted place formats with examples. It does not explicitly distinguish itself from siblings like preco_m2 or ranking_bairros, so it stops short of a perfect score.

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 intended use is implied: when the agent needs a side-by-side comparison of multiple cities or neighborhoods by price per square meter. However, there is no explicit 'when not to use' guidance, and no alternative tool is named for single-place price queries or ranking queries.

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

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

Annotations already indicate readOnlyHint=true, so no side-effect warning is needed. The description adds useful transparency by mentioning that the tool covers limitations of the number, which helps set expectations about the nature of the answer.

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 sentence that front-loads the core content — methodology, coverage, collection month, and limitations — and then gives a direct usage instruction. Every clause contributes useful information with no 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 zero-parameter informational tool, the description fully explains what the tool covers and when to use it. The absence of an output schema is not a gap here because the tool's purpose is to provide explanatory content, which the description already characterizes.

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 tool has zero parameters, so there is no parameter information for the description to add. The baseline of 4 applies because parameter semantics are not applicable and the description does not need to compensate for schema gaps.

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 states the tool explains how the Metro Quadrado index is calculated, which cities and property types it covers, the collection month, and the number's limitations. This makes it distinct from sibling tools like preco_m2 or ranking_bairros, which are about specific lookups or calculations.

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 explicitly says 'Use quando perguntarem de onde vem o dado,' giving a clear trigger condition. It does not explicitly say when not to use it or name alternatives, but the context is unambiguous enough for an agent to route to this tool.

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

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

Annotations already declare readOnlyHint=true, so the description doesn't need to restate safety. It adds behavioral value by disclosing the conditional behavior when bairro is provided – the typical property and comparison with the city median – which is not otherwise represented in schema or annotations.

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, front-loaded with the core purpose, followed by the conditional nuance. No filler or redundant restatement of the title.

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?

With no output schema, the description explains the main return values at a useful level. It doesn't specify units (e.g., R$/m²) or response structure, but for a read-only, moderately complex tool with richly documented parameters, it is close to complete.

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 coverage is 100%, so the baseline is 3. The description adds semantics for the bairro parameter by specifying that supplying it triggers additional output (typical property and city comparison), which enriches the schema's bare optional flag.

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 states a specific function: returning the median price per square meter for a city or neighborhood, filtered by property type and sale/rental operation. It goes beyond the title by detailing the conditional extra output (typical property and city comparison) when a bairro is given, but it does not explicitly distinguish itself from sibling tools like comparar.

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 context for when extra output appears (when bairro is informed), and implicitly signals it is the tool for median m² prices. However, it does not give explicit when-to-use/when-not-to-use guidance or reference any alternative sibling tools.

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

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

Annotations already declare readOnlyHint=true and the description does not contradict them. It adds the sorting orientation and the practical use-case, but it does not disclose limits, default ordering behavior, or what fields each ranked item contains.

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 with no redundancy; the core action and ordering are front-loaded, and the practical use-case completes the definition.

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 read-only listing tool with fully documented parameters, the description is nearly sufficient. It explains the output form (a list of neighborhoods sorted by m² price) and the motivating question, though it relies on the schema for city coverage and limits.

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 five parameters including defaults and enum options. The description adds only the general notion of ordering direction ('do mais caro ao mais barato (ou o inverso)'), matching the baseline for schema-heavy definitions.

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 concrete operation ('Lista os bairros de uma cidade') and the ranking attribute ('preço do m²'), and states the exact question it answers ('qual é o bairro mais caro ou mais barato'). This differentiates it from siblings like 'comparar' and 'preco_m2' by emphasizing an ordered ranking rather than a comparison or a single price lookup.

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 gives a clear intended use ('Serve para responder qual é o bairro mais caro ou mais barato'), so an agent knows when to pick it. It does not explicitly name alternatives or exclusion conditions, so it stops one step short of full routing guidance.

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

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

Annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds the formula's semantics but does not disclose output format, edge cases, or what happens when optional parameters are omitted. This adds some context beyond annotations but not rich behavioral detail.

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 two short sentences with no filler. The calculation is front-loaded, and the use case follows immediately, making it easy for an agent to scan.

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 simple read-only metric tool with all parameters documented in the schema, the description is largely complete: it gives the formula and the decision context. Since there is no output schema, it could state the expected return format more explicitly, but the formula strongly implies a numerical yield value.

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 baseline is 3; the tool description adds no parameter-level meaning beyond what the schema already provides. The formula in the description clarifies the conceptual output, but it does not explain parameter formatting or behavior. The schema is mostly self-sufficient, though it contains an apparent mismatch between '6 cidades' and the 9 enum entries.

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 metric (gross annual rental yield) and gives an explicit calculation: 12 rents divided by sale price per median square meter. It also states the decision it supports, buying to rent in a neighborhood, which separates it from siblings like preco_m2 and ranking_bairros. It does not explicitly contrast itself with sibling tools, 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 description provides a clear when-to-use rationale: 'Serve para responder se compensa comprar para alugar num bairro.' This tells the agent when this metric is relevant. However, it does not mention when not to use it or point to an alternative sibling, so it lacks explicit exclusion guidance.

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