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

Annotations already declare readOnly/idempotent/closed-world, so safety is covered. The description adds genuine behavioral context beyond that: pagination via next_cursor, the meaning of quality codes ('G checked, Y suspect'), the caveat that latest-hour data is not quality-controlled, and a licensing/attribution obligation ('Attribution required when the data is shown'). That is meaningful added value, though no rate limits or error behavior are mentioned.

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?

Purpose and pagination are front-loaded before the field glossary, and each clause carries useful information (field types, code mappings, attribution). It is dense and somewhat long, but no sentence is pure filler; the field list earns its place by explaining undocumented enums.

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 no output schema, the description compensates by enumerating the returned fields and their semantics, plus pagination and the quality/attribution caveats. Combined with a 100%-covered input schema, an agent has everything needed to call and interpret this tool.

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. The description exceeds it by decoding the parameter values (1 temperature, 4 wind speed, 6 humidity, 7 precipitation) and the quality flags, which the schema does not document ('Parameter (equals)' is opaque). The date-range and filtering params still rely on the schema alone.

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 title/description pair states a specific resource and granularity ('Weather per hour and station') and the body clarifies it returns a list of hourly weather records with named fields. The agent can identify it as a historical/observational weather query. It does not explicitly distinguish itself from the sibling weather_now, so differentiation is only implied by the name.

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 statement of when to use this tool versus weather_now or any other sibling, nor any prerequisites or exclusions. It mentions passing next_cursor for the next page, which is operational guidance, but not selection guidance. Usage must be inferred entirely.

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