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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable details: all 7 species are always included, out-of-season species show a 'no-current-data' status, and ambrosia is excluded because it is forecast-only. This goes beyond the schema and clearly explains edge cases and limitations, giving the agent a realistic expectation of the output.

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 three sentences long, with the primary purpose front-loaded in the first sentence. The following sentences add crucial context (species list, out-of-season behavior, exclusion) without any filler. Every sentence earns its place, making it both concise and 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?

An output schema exists, so return value details are covered separately. The description covers the station network size, exact species measured, the always-included behavior with a no-data status, and an explicit exclusion. It gives a complete picture of what the tool does and its quirks, leaving no significant gaps for an agent to fill.

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% for the only parameter ('station'), and the schema description already explains omission behavior. The tool description does not add any extra parameter-specific semantics; it remains entirely at the data-level. A baseline score of 3 is appropriate since the schema handles the parameter documentation adequately.

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's function: 'Get current pollen concentration data from MeteoSwiss monitoring stations.' It specifies the resource (MeteoSwiss stations), the action (get), and the scope (current pollen concentration, ~15 stations). It also distinguishes from siblings by listing the 7 measured species, making it unmistakable for pollen data.

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 a clear use case: 'Useful for allergy sufferers.' While it doesn't explicitly contrast with sibling tools (e.g., meteoswissCurrentWeather) or state when not to use it, the domain is so specific that the usage context is clear. No explicit alternatives or exclusions are mentioned, but the context is sufficient for an agent to decide.

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