swiss_hydro_data

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It states the tool fetches hydrological data but does not disclose whether it requires setup/api key, rate limits, whether any station_id is required (0 required parameters), what happens with no station_id, or what the return payload looks like. For a data-fetching tool with zero annotation coverage, this is a notable gap.

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 clean sentence that names the tool's purpose, the specific data categories, and the data source institution. Zero waste — every word earns its place while remaining informative.

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?

Given there's no output schema, no annotations, and no behavioral caveats, the description is thin on what the agent can expect. The tool is a single-purpose data fetcher, which reduces complexity, but the absence of any return format hints, error conditions, or setup requirements makes it underspecified relative to its informational burden. The 100% schema coverage and single optional parameter mitigate this somewhat, but the description should convey more about behavior for a no-annotation tool.

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% with a single parameter (station_id) that has a clear description including an example ('2104' with the Rhône at Gletsch station). With an optional parameter (0 required), the description's mixed data types help convey breadth. The schema already does the heavy lifting for this parameter, and the description's general scope adds appropriate context without redundancy.

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 a specific verb+resource — 'Get hydrological data' — and enumerates the specific data types: river levels, flow rates, flood forecasts. It clearly identifies the source as BAFU. However, it doesn't distinguish itself from sibling tools (e.g. swiss_weather_search_meteoswiss, swiss_avalanche_data) beyond the general hydrological scope, though the BAFU attribution provides reasonable differentiation.

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 no explicit when-to-use or when-not-to-use guidance, no discussion of alternatives, and no prerequisites (e.g., need for an API key via swiss_set_api_key). The context of 'hydrological data from BAFU' is clear but there is no guidance on distinguishing this from weather or avalanche data siblings, nor any mention of required setup.

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