seasonal_decompose

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 decomposition action but lacks critical details: whether it's read-only or mutative (though 'decompose' suggests analysis), what algorithm or assumptions are used (e.g., additive/multiplicative model), output format, error handling, or performance considerations. For a tool with no annotations, this leaves significant behavioral gaps.

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, efficient sentence that front-loads the core action and output. The domain/category tag adds context without verbosity. Every part earns its place, though it could be more informative without sacrificing conciseness.

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 complexity (time series decomposition), lack of annotations, 0% schema coverage, and no output schema, the description is insufficient. It doesn't cover behavioral traits, parameter details, output structure, or usage context. For a statistical analysis tool with two required parameters, this leaves too many gaps for effective agent use.

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 0%, so the description must compensate for undocumented parameters. It mentions 'time series' and 'components', which loosely relates to 'data' and 'period', but doesn't explain parameter meanings, units, constraints (e.g., period must be integer > 0), or data format expectations. The description adds minimal semantic value beyond the bare 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 the verb ('decompose') and resource ('time series'), specifying the output components (trend, seasonal, residual). It distinguishes the tool's purpose from many siblings (e.g., 'detect_seasonality', 'detrend', 'deseasonalize') by focusing on full decomposition rather than detection or removal. However, it doesn't explicitly differentiate from all potential analysis siblings in the extensive list.

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 minimal guidance, only implying usage for time series analysis via the domain/category tag. It doesn't specify when to use this tool versus alternatives like 'detect_seasonality', 'detrend', or 'deseasonalize', nor does it mention prerequisites (e.g., data must be periodic) or exclusions. No explicit when/when-not or alternative tool references are provided.

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