ai_anomaly_detection__zscore_anomalies

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

With no annotations, the description must cover behavior. It explains the algorithm and what is returned, but lacks details on edge cases (e.g., empty array, non-numeric values) or potential side effects. For a simple statistical function, it is adequate but not comprehensive.

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?

A single sentence front-loaded with the domain tag, concise and efficient. Every word adds value without redundancy.

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?

For a simple statistical tool, the description covers the core logic and output (indices). However, without output schema, it could specify the return format (e.g., list of integers). Edge cases and limitations are missing, but the complexity is low.

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 0%, so description must explain parameters. It clarifies 'threshold' as standard deviations, but does not specify the expected type of 'values' (presumably numeric array) or any defaults. This leaves ambiguity for the agent.

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 returns 'indices of values beyond threshold standard deviations from the mean,' specifying the exact statistical test. This distinguishes it from sibling tools like IQR (uses interquartile range) and rolling deviation, which use different methods.

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 on when to use Z-score over alternatives (e.g., IQR for skewed data). The description does not mention assumptions like normal distribution or provide context for selection among similar anomaly detection tools.

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