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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds useful behavioral context: newest-first ordering, each note carries the day it describes, and notes are editor-reviewed human accounts rather than raw measurements. This goes beyond the annotation baseline and helps the agent treat the output appropriately.

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?

The description is moderately long but front-loaded with the core purpose and then adds usage guidance and warnings. There is minor redundancy between 'what people who were actually there wrote' and 'somebody's own sentence about their own day', but overall every sentence contributes orientation or usage context.

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 simple read-only tool with two parameters and no output schema, the description covers the kind of content returned, ordering, and how to interpret it. The main missing pieces are explicit parameter documentation (especially limit) and a more concrete statement about the response shape, but these are low-stakes gaps given the simplicity and read-only annotation.

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 must compensate. It only implies the resort parameter through 'about a resort' and is silent about the limit parameter, its default, or how the limit affects the returned list. This leaves the agent to guess at the meaning of half the tool’s 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 clearly states the tool returns skier-written short notes/reviews about a resort, newest first, each tied to a day. It explicitly distinguishes this from models/measurements by calling it 'the one lane on Skinow that is not a model and not a measurement', which separates it from siblings like conditions and snow_forecast.

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?

It gives explicit use cases: 'queues, beginner-area ice, bus actually ran' – the things no forecast holds. It also warns not to present the notes as conditions. It does not name an alternative tool, but the contrast with forecasts and measurements is clear enough for an agent to route correctly.

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