detrend

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 removes trends to achieve stationarity, implying a transformation, but does not describe how the trend is removed (e.g., linear regression, differencing), what happens to the input data (e.g., modified or returned as residuals), or any limitations (e.g., assumes specific trend types). This leaves significant gaps for a mutation-like operation.

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. The domain and category annotations are efficiently appended. There is no wasted text, though it could benefit from more detail given the tool's complexity.

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 (transformation of time series data), lack of annotations, no output schema, and low schema description coverage, the description is insufficient. It does not explain the method of trend removal, output format (e.g., residuals array), or behavioral implications, leaving the agent with inadequate information to use the tool effectively.

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%, with one parameter ('data') documented only as an array of numbers in the schema. The description adds no information about this parameter, such as what 'data' represents (e.g., time series values), expected format (e.g., ordered sequence), or constraints (e.g., minimum length). It fails to compensate for the lack of schema descriptions.

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 trend from time series to obtain stationary residuals.' It specifies the verb ('Remove'), resource ('trend from time series'), and outcome ('stationary residuals'), and includes domain/category context. However, it does not explicitly differentiate from sibling tools like 'detect_trend' or 'deseasonalize', 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 mentions the domain ('timeseries') and category ('analysis'), but does not specify prerequisites (e.g., requires a time series with a trend), when-not-to-use scenarios (e.g., for non-time-series data), or refer to related tools like 'detect_trend' or 'deseasonalize' for context.

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