get_hospitalization_trends

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

With no annotations present, the description carries the burden. It discloses specific data quirks (UF interpretation in 1992–1997 and attribution of missing dates to billing month) and references get_available_years and notes for further details, which is above average but not exhaustive.

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 concise, using three sentences to convey purpose, key caveats, and references. It is well organized and free of redundancy, though it could slightly benefit from explicit output description.

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 does not detail the output structure (e.g., fields in the returned series), but it points to get_available_years and notes, which may fill that gap. Given the absence of an output schema, this is a minor shortfall but not critical.

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 schema already provides descriptions for all 5 parameters (100% coverage), and the description adds valuable context, particularly the special meaning of `uf` for early years and the default granularity. This enriches understanding beyond the schema alone.

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 returns time series of hospitalizations ('séries temporais de internações') with optional monthly/yearly granularity, effectively distinguishing it from sibling tools like get_hospitalizations (raw records) or get_hospitalization_rates.

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 mentions usefulness for trend and seasonality analysis, and provides important caveats about pre-1998 data. However, it does not explicitly contrast with alternative tools, though the time-series focus is implied.

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