climate_trend

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

Annotations already declare readOnly, idempotent, and closed-world behavior, so the bar is lower. The description adds genuinely useful behavioral detail beyond that: aggregation semantics differ by variable (totals for rain/snow/precipitation hours/evapotranspiration, means otherwise) and partial years are skipped, which affects result interpretation.

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?

Three short sentences with the core purpose front-loaded and no filler. The aggregation rule sentence is dense but each clause earns its place by preventing misinterpretation of the returned numbers.

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 shape need not be described. However, with 0% schema coverage the definition still leaves the agent guessing at valid variable values and station format, which are the two required parameters — a meaningful gap for a tool whose whole purpose is selecting one of each.

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 carries the burden. It does name four variables that behave as totals and clarifies the station/variable scope, which is real added meaning, but it never enumerates valid variable names, does not explain the station identifier format, and says nothing about start_year/end_year beyond their obvious names.

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 states a specific verb and resource: it returns yearly values plus a linear trend per decade, scoped to one station and one variable. The 'one station' scoping implicitly distinguishes it from compare_stations, though no sibling is named explicitly.

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?

Usage is only implied by the 'one station and variable' scope — an agent can infer this is the single-station trend tool rather than the multi-station comparison. There is no explicit when-to-use statement, no mention of alternatives like compare_stations or get_daily_weather, and no prerequisites.

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