detect_anomalies

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses the algorithmic approach (z-score and/or IQR) and implies a read-only analysis through the word 'detects', but it does not explicitly state side effects, data requirements, or how anomalies are returned. The disclosure is partial but not contradictory.

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, efficient sentence with no filler or repetition. It front-loads the core purpose and method. It is concise, though slightly under-specified for a tool with four parameters and no annotations.

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 absence of annotations and 0% schema description coverage, the description is not complete enough for confident invocation. It does not explain how columns should be provided, what threshold=0 means as a default, whether the tool modifies data, or what the anomaly result contains. The output schema covers returns, but the input side remains ambiguous.

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 needed to compensate for explaining parameters like columns and threshold. It briefly clarifies the method via 'z-score and/or IQR', but the meanings of threshold, columns format, and default behavior are left unspecified. The schema provides titles/defaults/enums, but not enough semantic depth.

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 that the tool detects outliers using z-score and/or IQR, which is concrete and non-tautological. It does not explicitly name the target resource or contrast with siblings like detect_patterns or statistical_test, so it stops short of full 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?

No guidance is given about when to use this tool instead of alternatives, how to choose between zscore, iqr, and both, or what prerequisites exist (e.g., numeric columns). The purpose implies an anomaly-detection scenario, but no explicit when-to-use/when-not-to-use information is provided.

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