detect_trend

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 mentions the method ('linear regression') but does not describe key behavioral traits such as what the output looks like (e.g., slope, intercept, p-value), whether it handles missing data, assumptions about the time series (e.g., equally spaced points), or any limitations (e.g., sensitivity to outliers). For a tool with no annotations, this leaves significant gaps in understanding how it behaves.

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 highly concise and well-structured, consisting of a single sentence that efficiently conveys the core purpose and context. It uses no unnecessary words and front-loads the essential information, making it easy to parse quickly. Every part of the sentence earns its place by specifying the action, resource, method, domain, and category.

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 trend detection in time series, the description is incomplete. There is no output schema, and the description does not explain what the tool returns (e.g., trend metrics or statistical results). Combined with no annotations and poor parameter semantics, this makes it difficult for an agent to understand how to use the tool effectively or interpret its results, despite the concise purpose statement.

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 one parameter ('data') with 0% description coverage, meaning the schema provides no semantic information. The description does not compensate by explaining what 'data' should contain (e.g., an array of numeric values representing time series points, expected format, or constraints like minimum length). This leaves the parameter's meaning unclear, which is inadequate given the low schema coverage.

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 and quantify trend in time series using linear regression'. It specifies the verb ('detect and quantify'), resource ('trend in time series'), and method ('linear regression'), making it easy to understand what the tool does. However, it does not explicitly differentiate from sibling tools like 'linear_regression' or 'detrend', which are related but not identical, preventing 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 minimal usage guidance. It mentions the domain ('timeseries') and category ('analysis'), which implies context, but does not specify when to use this tool versus alternatives like 'linear_regression' (which might perform regression without trend detection) or 'detrend' (which might remove trends). There are no explicit instructions on when or when not to use it, leaving the agent to infer usage from the purpose alone.

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