find_anomalies

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

With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions 'multiple detection methods' but doesn't explain what these methods do, their computational characteristics, or what 'comprehensive anomaly detection results' entails. No information about performance, side effects, or limitations is provided.

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 brief (two sentences) and front-loaded with the core purpose. However, the second sentence about return values is somewhat redundant given the existence of an output schema. While efficient, it could be more structured with clearer separation between purpose and behavioral 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 tool's complexity (anomaly detection with multiple methods), no annotations, but 100% schema coverage and an output schema, the description is minimally adequate. It identifies the core function but lacks important context about method differences, performance expectations, and comparison to sibling tools. The output schema reduces but doesn't eliminate the need for behavioral explanation.

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 100%, so the schema already fully documents all three parameters (columns, sensitivity, methods). The description adds no parameter semantics beyond what's in the schema - it doesn't explain how these parameters interact, what 'sensitivity' means in practice, or provide examples of method combinations. Baseline 3 is appropriate when schema does all the work.

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 'Find anomalies in the data using multiple detection methods' which provides a clear verb ('find') and resource ('anomalies in data'), but it's somewhat vague about what constitutes 'anomalies' and doesn't distinguish from sibling tools like 'detect_outliers' or 'check_data_quality'. It doesn't specify what type of data anomalies are being detected (e.g., statistical outliers, missing values, pattern deviations).

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 like 'detect_outliers' or 'check_data_quality'. There's no mention of prerequisites, expected data format, or comparative strengths/weaknesses of different anomaly detection methods. The agent must infer usage context from the tool name alone.

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