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

Annotations already mark the tool as read-only, idempotent, and non-destructive, so the description's job is to add behavioral context. It does: returns data from the nearest station with a record, includes station distance, covers any date by day of year, and explicitly states these are long-period averages, not forecasts or single-year observations. This goes well beyond the annotation metadata.

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?

Every sentence earns its place: definition, return values, usage trigger, pairing guidance, and the day-of-year caveat. The most important scoping information is front-loaded in the first sentence, and despite its length, the description remains efficient and readable.

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 read-only tool with no output schema, the description fully covers what an agent needs to select and call it correctly: what it returns, when to use it, how it relates to forecasts, and the key conceptual caveat about day-of-year averaging. No critical decision-relevant context is missing.

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%, so the baseline is 3, but the description adds temporal semantics not present in the schema: dates are interpreted by day of year, making any past or future date valid. It also clarifies the meaning of the returned 'normal' values in terms of 30-year averages, which helps the agent reason about start/end/days parameters.

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 definition: 'Day-of-year climate normals (NCEI 1991-2020 30-year averages) for a US location, from the nearest station with a record.' It names the resource (climate normals), the scope (US location), and the return values (normal high, low, mean, station and distance). It also distinguishes itself from siblings by noting it is not a forecast and not observed history for a specific year, which differentiates it from get_forecast and get_climate_records.

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 explicitly says when to use it: 'Use this whenever a question needs a baseline rather than a forecast,' with concrete examples. It also gives pairing guidance: 'Pair it with get_forecast to say how far above or below normal the coming days run.' This gives the agent clear decision criteria versus alternatives.

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