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

The description adds meaningful behavioral detail beyond the readOnly/idempotent annotations: it 'scans the forecast,' returns 'daylight-aware periods,' and requires periods to match 'all conditions.' This clarifies that the tool performs analysis and filtering rather than a simple lookup. It does not conflict with annotations.

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 the core purpose, then the key behavioral refinement, then a compact example. The example is long but earns its place by demonstrating the nested criteria structure. There is no filler or repetition of schema details.

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?

The description is largely complete given the output schema and rich parameter schema: it covers units, daylight-awareness, and required criteria matching. One communication gap remains: 'optimal' is not defined (e.g., earliest window? longest? best combined score?), and there is no explicit note about behavior when no window is found. These are not fatal but keep it from being fully comprehensive.

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 a baseline of 3 applies, but the description adds a concrete JSON example showing how criteria, location, hours, and activity_duration_hours fit together. This is especially helpful because criteria is a nested object with multiple optional fields. The example clarifies the intended usage pattern beyond 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 description states a specific verb and resource: 'Find the optimal time window for an activity based on weather criteria.' It clearly distinguishes the tool from raw forecast/data-access siblings by emphasizing criteria-based window selection, daylight awareness, and matching all conditions. This makes its purpose unmistakable.

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 gives clear context for when to use the tool: when an activity needs a weather-compliant time window rather than just forecast data. It does not explicitly name alternatives or exclusion conditions, but the behavioral contrast with the many raw get_* forecast tools is apparent. A small missed opportunity is not saying 'use get_forecast if you only need raw conditions.'

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