A Corrida dos Bairros: preço do m² por bairro

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 mark the tool as read-only and not open-world, and the description adds meaningful informational context: it promises to disclose limitations, coverage, and the collection month of the index. No side effects or data mutations are implied, so there is no contradiction.

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?

A single, compact sentence front-loads the tool's subject and lists the covered topics, then closes with a direct usage instruction. Every clause earns its place; there is no redundant restating 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?

For a parameterless informational tool with no output schema, the description fully specifies the available content: methodology, cities and property types covered, collection month, and limitations. The trigger condition is also included, making the definition self-sufficient.

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 and 100% schema description coverage, so no parameter documentation is needed. The baseline for parameterless tools is 4; the description could not add parameter-level value.

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 subject, the A Corrida dos Bairros index methodology, and enumerates exactly what it covers: calculation, cities, property types, collection month, and limitations. This clearly differentiates it from the sibling data-operation tools.

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 an explicit trigger condition: 'Use quando perguntarem de onde vem o dado' (use when they ask where the data comes from). This is an actionable, unambiguous usage rule that tells the agent the intended context.

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?

The readOnlyHint and openWorldHint annotations already indicate the tool is safe and constrained. The description adds useful behavioral context: it states that when a bairro is provided, the response includes the typical property and comparison with the city median. It does not disclose rate limits, auth, or any other operational details, but the annotation covers the safety profile. This is a moderate addition beyond 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?

The description is two sentences with zero redundancy. The core purpose is stated in the first sentence, and the conditional behavior for bairro is added in the second. It is front-loaded and every sentence contributes. There is no extraneous information.

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 tool with 4 parameters (1 required) and no output schema, the description explains the output (median price, and additional data when bairro is given) and the filtering dimensions. It does not describe the return format or structure, but given the simplicity of the tool, this is adequate. The absence of explicit guidance on siblings is a minor gap, but the tool's scope is clear.

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% for all parameters, including defaults for tipo and operacao. The description does not add any parameter-specific meaning beyond what the schema already provides. It mentions the dimensions (tipo, operacao, bairro) but not in a way that enhances the schema. This meets the baseline for high coverage.

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 precisely what the tool returns: the median price per square meter for a city or neighborhood, broken down by property type and operation (sale/rental). It also mentions the additional data when a neighborhood is provided. This clearly distinguishes it from siblings like ranking_bairros or rendimento_aluguel.

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 explains what the tool does but does not provide any explicit guidance on when to use it versus alternative tools. There is no mention of sibling tools or exclusion conditions. The only conditional is 'Quando o bairro é informado', which is about parameter usage, not tool selection. An agent would need to infer usage from the purpose.

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 mark the tool read-only; the description adds relevant behavioral context: it covers only apartments, it accepts city or neighborhood scope, and the data is a dated cut ('Recorte publicado em 07/09/2026'). It does not describe the result format, but no output schema exists and the read-only profile reduces the burden.

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 short sentences, each earning its place: the main output and filters, the use case, and the data vintage. It is front-loaded and free of repetition.

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 lookup with three parameters and no output schema, the description covers what is returned, the available filters, the apartment-only scope, the use case, and the publication date. Nothing essential is missing 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 coverage is 100%, but the description earns extra credit by decoding the opaque faixa enum values into understandable ranges (up to 50, 50-70, etc.) and by stating the city/neighborhood scoping. This adds meaning the schema alone does not convey.

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 first sentence states precisely what the tool returns: price per m² and typical apartment by area bracket, at city or neighborhood level. 'Só apartamentos' and the explicit range list differentiate it from general price tools like preco_m2 even without naming siblings.

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 use case: comparing neighborhoods without mixing apartment sizes, and explains why the faixa dimension matters ('o bairro mais caro por m² muda conforme a faixa'). It does not name alternatives or state when not to use it, so it stops short of a 5.

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 openWorldHint=false, so the safety profile is covered. The description adds behavior by clarifying the output is a ranked list and that ordering can be reversed, but it does not disclose additional behavioral details such as default limits or result structure; the schema covers parameter-level behavior.

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 compact and front-loaded, with the primary action stated first and the use case in a short second sentence. There is no filler or redundant content, and every sentence contributes useful context.

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, combined with the rich schema and annotations, is enough for an agent to understand what the tool returns and when to use it. It could be slightly more complete by naming sibling alternatives or clarifying that the result includes the m² price value, but the core invocation context is adequately covered.

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 fully documents all five parameters, including enums, defaults, and the 'limite' range. The description adds no parameter-level detail beyond what the schema already provides, making the baseline score of 3 appropriate.

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 and resource: 'Lista os bairros de uma cidade ordenados pelo preço do m²', with explicit mention of sort direction ('do mais caro ao mais barato ou o inverso'). It also states the intended use case, though it does not explicitly differentiate this tool from sibling tools such as preco_m2 or 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?

The phrase 'Serve para responder qual é o bairro mais caro ou mais barato' gives a clear intended application for the tool. However, it does not mention when to avoid this tool or what alternatives to prefer, so it stops short of full usage 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?

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.