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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, open-world), yet the description adds real behavioral context beyond them: an LLM may be invoked on a cold cache (latency/cost), a template fallback exists, the source field can be claude/openai/template, and returned units/direction convention. It stops short of saying how long the cold path takes or how long results stay cached.

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?

Four telegraphic sentences, front-loaded with the output shape, then fallback, source, rate limit, and units. Every sentence carries information, though the clipped phrasing ('Units: ft, kt, s, F, mi; directions FROM') is dense enough to be slightly harder to scan.

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 a single required parameter, no output schema, and annotations covering safety, the description supplies what is needed to call it correctly: what comes back, that a template fallback may occur, and the metadata accompanying the payload. Only the parameter's own semantics are left unaddressed.

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 reported at 0% and the description adds nothing about the spotId path parameter. However, the nested schema does carry a description ('Spot id, e.g. boca-inlet') and the parameter is self-evident from the title's 'for a spot', so the practical gap is small; baseline 3 applies.

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 precisely what the tool produces: 'a short headline + <=60-word body written from the live data', which distinguishes it from raw-data siblings like get_conditions, get_spot, or get_observations. It never names an alternative explicitly, but the synthesized-narrative nature of the output is unambiguous.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no sibling is named as an alternative (e.g. 'use get_conditions for raw numbers'). The rate-limit note hints that this is a heavier call, but the agent must infer when a briefing is preferable to pulling the underlying datasets.

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