env_hydro_stations

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

Adds rich behavioral context: ~260 stations, measurement interval, fallback to example stations on LINDAS failure (recognizable via provenance), and match_type 'none' for empty results. These details go well beyond the readOnlyHint annotation.

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?

Structured with a clear summary, use_case, and important_notes. Front-loaded and efficient; the length is justified by the need to explain the unsupported canton and fallback behavior.

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?

Covers listing, filtering, unsupported parameters, fallback behavior, and return format. With an output schema available, the description is complete for effective use.

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 description includes an Args section listing all parameters and notes canton is currently unavailable, water_body is the supported filter, and response_format accepts markdown or json. Given schema description coverage is 0%, this compensates well, though it could elaborate on response_format behavior.

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?

Description clearly states it lists BAFU hydrological stations at Swiss rivers and lakes, and distinguishes it from env_hydro_current by positioning it as the station-finding step before retrieving current values.

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?

Provides an explicit use case ('Hydromessstationen finden... um danach mit env_hydro_current Pegel/Abfluss abzurufen') and clearly states the canton filter is not supported, recommending water_body instead. It does not explicitly compare to other siblings like env_hydro_history, but the context is clear.

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