detect_seasonality

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds behavioral value beyond those annotations by revealing the autocorrelation method and the specific output categories, giving the agent a concrete sense of what the analysis returns. No contradiction exists.

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?

Two sentences with no filler, front-loaded with the core verb and resource, and the second sentence adds concrete output expectations. Every word earns its place.

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 read-only analysis tool without an output schema, the description summarizes what an agent can expect (periods and peak hours) while params are fully covered by the schema and annotations cover the safety profile. It could be more explicit about return format or data requirements, but nothing critical blocks a correct call.

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 schema already documents all five parameters (device_id, datapoints, hours_back, instance_id, device_datasource_id). The description does not add parameter-level meaning, which is acceptable at the baseline given the high schema coverage.

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 ('detect'), a clear resource ('metric data'), and the method ('autocorrelation'). It further specifies concrete outputs (dominant periods such as 1h/24h/168h and peak activity hours), which clearly differentiates it from sibling analytics like forecast_metric or detect_change_points.

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 provides clear context: this tool is for periodic-pattern/seasonality analysis rather than general anomaly detection or forecasting. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full routing guidance.

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