detect_outliers

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 methods (z-score, IQR) but does not describe what the tool returns (e.g., list of outliers, indices, scores), error handling, performance characteristics, or any constraints. This is a significant gap for a tool with no annotation coverage.

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, consisting of a single sentence that directly states the tool's purpose and methods. There is no wasted text, and it efficiently conveys the core functionality without unnecessary details.

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 (statistical outlier detection with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It does not explain the return values, error cases, or practical usage details. The description fails to provide sufficient context for effective tool invocation by an AI agent.

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 0% description coverage, so the description must compensate. It mentions 'z-score or IQR method' which relates to the 'method' parameter, but does not explain the 'numbers' parameter (an array of strings representing the dataset) or the 'threshold' parameter (default 3, likely for z-score). The description adds minimal semantic value beyond the schema.

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 outliers in a dataset using z-score or IQR method.' It specifies the action (detect outliers), resource (dataset), and methods (z-score, IQR). However, it does not explicitly differentiate from sibling tools, which are primarily mathematical functions rather than statistical detection tools, so it lacks explicit sibling differentiation.

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 methods (z-score, IQR) but does not specify contexts, prerequisites, or exclusions. There is no mention of when to choose one method over the other or how it compares to other statistical tools in the sibling list.

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