anomaly_detection

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

With no annotations provided, the description carries the burden of disclosing behavior. It does state 'Read-only' and gives the exact detection rules (|z| > threshold and 1.5 * IQR bounds), which is useful. However, it does not mention input requirements such as numeric columns, how missing values are handled, or whether results are returned as indices/rows/values, though the output schema may cover some of this.

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 sentences with no filler. The core action and algorithm details are front-loaded, and the read-only note is placed efficiently at the end. Every sentence contributes information worth having.

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 description covers the core algorithm and the read-only nature, and an output schema exists to define return values. However, it lacks guidance on when to choose this tool over ml_anomaly_detection, and it does not mention necessary input preconditions like numeric column data. Some contextual gaps remain that an agent would need to resolve elsewhere.

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 for missing parameter documentation. It adds meaning for 'column', 'method', and 'threshold' by explaining how they are used in the algorithms. However, 'file_path' and 'sheet_name' are only present in the schema titles and receive no descriptive treatment, and the allowed values for 'method' are not enumerated beyond the two names in the text.

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 'Detect anomalies' and names the resource ('a column') plus the two concrete methods (z-score and IQR), so the tool's function is clear. It does not explicitly distinguish itself from the sibling ml_anomaly_detection, but the statistical method detail makes the intended scope reasonably evident.

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 implies a statistical anomaly-detection use case and notes that the operation is read-only, but it gives no explicit guidance on when to use this tool instead of ml_anomaly_detection, summary_statistics, or data_quality_report. An agent would have to infer the appropriate context from the method names.

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