detect_seasonality

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 states the tool detects seasonal patterns, implying a read-only analysis function, but lacks details on computational behavior, output format, error handling, or limitations (e.g., data length requirements). This is inadequate for a tool with parameters and no output schema.

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, consisting of a single sentence that directly states the tool's purpose. It avoids unnecessary words, though it could be more informative without sacrificing brevity. The parenthetical domain and category add minor context without clutter.

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 analysis with parameters), lack of annotations, 0% schema coverage, and no output schema, the description is insufficient. It does not explain what 'seasonal patterns' means, how results are returned, or any assumptions about the input data, leaving critical gaps for effective 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 does not mention the 'data' parameter (required array of numbers) or 'max_period' (optional integer with default 12). No additional meaning, constraints, or examples are provided, leaving parameters semantically unclear.

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: 'Detect seasonal patterns in time series data.' It specifies the verb ('detect'), resource ('seasonal patterns'), and domain context ('time series data'), making the intent unambiguous. However, it does not differentiate from sibling tools like 'seasonal_decompose' or 'seasonal_strength', which are related but not explicitly contrasted.

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 mentions the domain ('timeseries') and category ('analysis'), but does not specify scenarios, prerequisites, or exclusions. For example, it does not indicate whether it's for preliminary analysis or when other seasonal tools might be more appropriate.

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