deseasonalize

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It fails to describe any behavioral traits: it does not explain what algorithm is used (e.g., moving average, STL), whether the operation is destructive or reversible, what the output format is (e.g., deseasonalized series, residuals), or any error handling. This leaves the agent with insufficient information to understand the tool's behavior beyond its basic purpose.

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 concise and front-loaded, stating the core purpose in a single sentence with no wasted words. The domain and category tags are efficiently appended. However, the brevity comes at the cost of completeness, as it omits necessary details for a tool with no annotations or output schema.

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 complexity of time series analysis, lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It fails to address key aspects such as the algorithm, output format, error conditions, or usage context. While concise, it does not provide enough information for an agent to effectively use this tool in practice.

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 has 0% description coverage, so the description must compensate. It mentions 'time series' and 'seasonal component', which loosely relate to the 'data' and 'period' parameters, but does not explain their semantics (e.g., 'data' as an array of time series values, 'period' as the seasonal cycle length). Without details on units, constraints, or examples, the description adds minimal value beyond the schema's structural information.

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's purpose: 'Remove seasonal component from time series' with the domain and category specified. It uses a specific verb ('Remove') and identifies the resource ('seasonal component from time series'), making the intent unambiguous. However, it does not explicitly differentiate from sibling tools like 'detect_seasonality' or 'seasonal_decompose', which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. It lacks any mention of prerequisites (e.g., needing detected seasonality first), exclusions, or comparisons to siblings such as 'detect_seasonality' or 'seasonal_decompose'. The domain and category tags offer minimal contextual hints but no explicit usage instructions.

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