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

Description adds behavioral context beyond annotations: mentions the large dataset (100K+), return fields (ID, name, coordinates, elevation, coverage dates), and historical depth (some from 1700s). Annotations already indicate read-only, idempotent, non-destructive behavior.

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?

Two sentences with no fluff: first sentence defines purpose and scope, second explains return values and downstream usage. Information is front-loaded and each sentence earns its place.

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?

Covers key return fields and usage context; might benefit from mentioning default limit (10) but overall sufficient given schema and annotations. Output schema exists but isn't detailed here; not required.

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 covers 100% of parameters with detailed descriptions (e.g., location_id format examples). Description only broadly restates 'by location' without adding new semantics. Baseline 3 is appropriate.

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?

Description clearly states verb 'Search', resource '100K+ global weather stations', and specific location criteria. It also explains the return fields and downstream usage with ncei.daily_data, distinguishing it from sibling tools.

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?

Explicitly states when to use (to obtain station IDs for historical data) and recommends pairing with ncei.daily_data. Does not provide explicit when-not-to-use scenarios, but context is clear for typical usage.

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