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

Although annotations already mark this as read-only/idempotent, the description discloses important behavioral details: series start, UF semantics for 1992-1997, and how missing source dates are bucketed. It also routes to notes and get_available_years for further caveats.

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 compact sentences put the core behavior and purpose up front, then add only essential historical caveats. There is no filler or repetition of schema content.

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 complex time-series tool with an output schema, it covers the main pitfalls: date range ambiguity, UF attribution, missing dates, and where to find authoritative notes. The references to get_available_years and notes close the remaining gaps.

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 description goes beyond the schema by explaining how uf behaves in the 1992-1997 window and linking the monthly/annual choice to the granularity parameter.

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-resource pair ('Retorna séries temporais de internações') and names the monthly/annual variants plus intended use ('análise de tendências e sazonalidade'). It is clear, though it does not explicitly contrast with sibling tools like get_hospitalizations or compare_icsap_trends.

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 states a use case ('útil para análise de tendências e sazonalidade') and points to get_available_years, but gives no when-to-use/when-not-to-use guidance or exclusions. The usage context is implied rather than explicit.

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