get_snow_conditions

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

With no annotations provided, the description carries the full disclosure burden and largely meets it: it reveals default behaviors (7 forecast days, hourly included, Europe/Zurich timezone), the enriched 'ski condition assessment' computation, and the exact return structure. It omits data-source provenance and latency, but for a read-only retrieval tool these are minor; no hidden mutation or destructive behavior exists to warn about.

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 content is useful and well-structured with bold headers, but it is redundant: the parameter list and returns are documented twice (once in the overview, again in the Args/Returns block). The same information spans roughly double the needed length, which dilutes front-loading.

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 5-parameter tool with zero schema descriptions and no annotations, the description is nearly self-sufficient: it explains all required and optional parameters, supplies defaults and examples, documents the nested return structure, and gives use cases. The only notable omission is guidance on coordinate validation or how this tool relates to get_weather when planning is not ski-specific.

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 0%, so the description must compensate, and it does: every parameter is explained with a natural-language meaning, a default value, and concrete examples (45.9763 / 7.6586 for Zermatt), plus the 1-16 range for forecast_days that the schema does not convey. Minor gap: latitude/longitude valid ranges are not stated.

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 opens with a specific verb+resource statement: 'Retrieves snow conditions and forecasts for mountain locations.' Combined with the use-case list (ski trip planning, resort checks, avalanche risk assessment), it clearly differentiates from siblings like get_weather and get_historical_weather by scope and location type.

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 'Use this tool for' section provides concrete invocation contexts (ski trip planning, checking resort conditions, mountain weather forecasts, avalanche risk assessment), which is clear contextual guidance. However, it never names alternatives or states when NOT to use it, so an agent could still confuse it with get_weather for general mountain forecasts.

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