detect_outliers

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

With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Detects', which implies a read-only analysis, but does not disclose the outlier detection method, whether the detection modifies the dataset, or what the return format looks like. This minimal information is insufficient for an agent to anticipate side effects or outputs.

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 a single sentence with no fluff, which is concise, but it is so terse that it sacrifices meaningful content. It earns its place structurally but fails to convey necessary details, making it less effective as a specification.

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 simple input schema (one parameter) and lack of output schema/annotations, the description is still inadequate. It does not explain return values, the nature of the outlier detection (statistical method), or any edge cases. This is barely more informative than naming the tool itself.

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. The only parameter 'path' is not elaborated; the description says 'in the dataset' but does not clarify what 'path' refers to (file path, DataFrame path, etc.). No additional semantics are added over the bare schema definition.

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 states a specific verb ('Detects') and resource ('Outliers'), clearly indicating the tool's function. It distinguishes from sibling tools which are focused on overviews, summaries, and missing values, not outlier detection. However, it is somewhat generic and does not specify the method or output.

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?

No guidance is provided about when to use this tool versus alternatives. It neither specifies a scenario nor excludes any. The description implies usage for outlier detection but offers no context about prerequisite steps, dataset expectations, or how this fits into the analysis flow.

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