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

Annotations already cover safety (readOnly/idempotent/non-destructive), and the description adds substantive context beyond them: the data is MODELED not measured, sourced from SLF SNOWPACK at three elevations, includes a reason and confidence, is surface-only, and returns 'not available' in unverified regions.

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?

Front-loaded with the payoff ('what the surface actually skis like'), then justification and caveats. The enumerated label list and the 30cm analogy are slightly expansive but each earns its place by clarifying the metric's value.

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?

With an output schema present, return-value detail is not required, and the description still covers the dataset origin, coverage caveat, scope limits, and alternatives — everything needed to invoke it correctly.

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 100%, so the baseline is 3. The description adds only marginal meaning (three elevation bands, headline label read from mid), and the mid default is already stated in the schema.

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?

States a precise verb+resource (surface snow-quality label at a resort) and enumerates the actual output categories, which lets an agent distinguish it from get_snow_report's snowfall totals. It also explicitly differentiates itself from get_avalanche.

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?

It frames the selection condition well — 'the question snowfall totals cannot answer' — and routes avalanche questions to get_avalanche. It stops short of an explicit 'use this instead of X when Y' statement for the full sibling set, but the routing is clear.

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