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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds substantial extra behavior: the same-ensemble comparison methodology (GFS→GEFS, IFS→ECMWF-ENS, ICON→ICON-EPS), the 60/25/15 weighting blend, and the interpretation of high vs low values.

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 one-line meaning of the metric, followed by compact rationale and usage paragraphs. The methodological middle paragraph is longer than strictly necessary but each sentence carries real interpretive 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 no output schema present, the description usefully documents the return shape — per-day mean and minimum confidence plus a per-variable breakdown at the least confident hour — and covers interpretation, so an agent has everything needed to call and read the result.

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% with 3 parameters, so the description must carry the load. It explains how the models parameter is interpreted (each deterministic model vs its own reference ensemble) but adds nothing about the latitude/longitude parameters or the accepted array format, so compensation is only partial.

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 specific resource — hour-by-hour forecast confidence derived from ensemble spread — and explicitly frames it as 'Solano's own indicator, not a raw model output,' which distinguishes it from sibling forecast tools like get_forecast and get_storm_risk.

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 concrete trigger phrases ('is this forecast reliable?', 'will it hold?') and a workflow cue ('before committing to a passage plan'), giving the agent clear context for invocation. It stops short of naming an alternative tool or stating when not to use it, so it is not a full 5.

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