classify_trend

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

Annotations already convey read-only, idempotent, and non-destructive behavior, so the description does not need to repeat those. It adds the statistical methods used (linear regression slope, coefficient of variation, autocorrelation), but does not disclose return shape, handling of multiple datapoints, or edge cases.

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 two tight sentences with no filler. It front-loads the action and output categories, then gives the relevant methodology in the second sentence.

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?

The tool is well annotated as read-only and has fully documented parameters, but there is no output schema and the description does not clarify whether the result is a single classification or a per-datapoint mapping. Since datapoints can be omitted to include all, this is a meaningful gap.

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 100%, so the parameters are already well documented. The description adds no parameter-specific detail beyond the schema, so a baseline score of 3 is appropriate.

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 uses a specific verb and resource ('Classify metric trends') and explicitly lists the five output categories, making the tool's purpose clear. It doesn't explicitly differentiate this from sibling analytics tools like detect_change_points or forecast_metric, but the core operation is unambiguous.

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?

There is no guidance on when to use this tool versus alternatives such as get_device_data, detect_seasonality, or detect_change_points. The description mentions methodology but provides no context, exclusions, or selection criteria.

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