metodologiaComo o Índice Metro Quadrado é apurado, quais cidades e tipos de imóvel cobre, qual o mês da coleta e quais as limitações do número. Use quando perguntarem de onde vem o dado.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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.
Add one secure layer between your agents and this server.
Each tool targets a clearly distinct query type: single-value lookup, side-by-side comparison, neighborhood ranking, rental yield, and methodology. There is little risk of selecting the wrong tool for a given user intent.
Names are readable and all lowercase Portuguese, but they mix conventions: 'comparar' is a bare verb, 'metodologia' is a simple noun, and the other three are compound nouns with underscores. This is not a consistent verb_noun pattern, though it remains understandable.
Five tools is well-scoped for a focused real-estate data server covering lookup, comparison, ranking, yield, and methodology. Each tool earns its place without redundancy or bloat.
The set covers the core user journeys for this domain: retrieving prices, comparing places, ranking neighborhoods, calculating rental yield, and explaining the data's provenance and limitations. No obvious dead ends or missing operations are apparent.