get_seasonality

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

With no annotations, the description must disclose behavioral traits. It only states the purpose, not how the tool behaves (e.g., read-only, aggregation method, data freshness, or side effects). The agent lacks critical context for safe invocation.

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 a single, front-loaded sentence with no wasted words. Every part adds value, stating the action, resource, and key dimensions concisely.

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 output schema exists and the input schema is well-described, the description covers the core purpose. However, it lacks any usage context or behavioral notes that would make it fully self-contained, so it is slightly above average but not complete.

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 input schema provides descriptions for all four parameters (lookback_days, granularity, metric, store), so the description adds minimal extra meaning. It implies the granularity dimension but does not detail each parameter. Schema coverage is effectively high, so a baseline of 3 is appropriate.

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?

Description clearly states the tool identifies seasonal patterns in store data by day of week, month, or quarter, specifying both the resource (store data) and the dimensions. It is distinct from sibling tools like forecast_demand or compare_periods, but does not explicitly differentiate them, preventing a 5.

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?

No guidance is provided on when to use this tool versus alternatives (e.g., forecast_demand for future predictions, compare_periods for period comparisons). There are no use case examples, prerequisites, or exclusions, leaving the agent to infer usage.

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