total_cloud_cover

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the input constraints (min/max offset of 2 to 121 hours), the return type (float), and explains the offset semantics with examples. It does not mention error handling or data availability edge cases, but for a simple weather lookup, the provided details are sufficient and not misleading.

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 compact and well-structured into paragraphs for purpose, arguments, return value, and examples. It front-loads the primary purpose and includes only relevant details. Each sentence adds value (constraints, examples) without redundancy. The formatting is clean and easy to parse.

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?

Given the tool's low complexity and the presence of an output schema, the description covers all necessary aspects: both parameters with constraints, the return value, and illustrative examples. The description is complete for an agent to call the tool correctly. No critical information (e.g., units, timezone) 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?

The schema provides only types (string, integer) with no descriptions, and schema_description_coverage is 0%. The description fully compensates by explaining that location is a place name, lead_time_swiss is an hour offset from 00:00 Swiss local time, and it specifies the valid range (2–121). The examples clarify the time interpretation, adding substantial meaning beyond the schema.

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 'Get total cloud cover percentage for a location at a specific offset time', specifying the exact resource (cloud cover percentage) and the parameters that distinguish it from siblings (location, specific offset time). The name matches the purpose, and the description includes the verb 'Get' and concrete examples that remove any ambiguity.

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 clearly indicates the tool is for retrieving cloud cover, but it does not explicitly compare to siblings like total_rainfall or temperature. However, the purpose is so specific that an agent can infer when to use it. The examples also illustrate valid call patterns, effectively showing when the tool applies, though no exclusions or alternative tools are named.

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