Air trend

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

Annotations already indicate readOnlyHint and openWorldHint. The description adds valuable context by detailing the output fields (mean, median, rate_of_change, etc.) and the optional natural-language observation, with no contradictions.

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 front-loaded with purpose and output structure, and every sentence adds value. While it is somewhat verbose, it remains clear and efficient for the complexity of the tool.

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 no output schema, the description adequately explains return fields and behavior (per-pollutant stats, worst_pollutant, optional observation). It covers the main scenarios but could clarify window application slightly more.

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 100%, so the description adds little beyond defaults and examples. Parameters are well-described in the schema, making additional detail in the description minimal.

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 performs windowed trend analysis for PM2.5, CO2, and VOC, and differentiates from sibling tools by focusing on trends over time. It provides specific output fields and usage examples like 'is PM2.5 climbing?'.

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 explicitly provides usage examples (e.g., 'is PM2.5 climbing?') and implies when to use this tool over alternatives like single current reading or daily summary, though it does not explicitly list when not to use it.

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