detect_outliers_resource

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

The description discloses the IQR method, sorting by distance from median, and caching behavior (first call downloads, subsequent calls reuse). Annotations already indicate readOnlyHint and openWorldHint, and the description adds value beyond them without contradiction.

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 very concise (5 lines) with clear structure: purpose, method, use case, caching. Every sentence adds value.

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 and existing output schema, the description covers caching, ordering, and use cases. It could mention handling of missing values or non-numeric columns, but overall it is informative enough.

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 coverage is 100%, so baseline is 3. The description does not add significant meaning beyond the schema for parameters; it only provides context on the method and output ordering.

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 it finds rows where a numeric column falls outside the IQR fence, specifies the method, and gives concrete use cases (salary, budget, census data). It distinguishes from sibling tools by focusing on outlier detection via IQR.

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 clear context (detecting data-entry errors) and mentions caching behavior. However, it does not explicitly state when not to use this tool or provide direct comparisons to siblings like quantiles_resource or filter_resource.

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