detect_anomalies

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the detection methods (statistical, ML, baseline), the parameters affecting behavior (sensitivity, timeframe), and the return structure (anomalies, baseline, confidence, recommendations). This provides substantial transparency beyond the tool name, though it does not explicitly state whether the tool is read-only or has side effects.

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 well-structured with a short opening sentence, a bulleted Args list, and a bulleted Returns list. Every sentence earns its place, providing necessary context without unnecessary filler. It is appropriately sized for a tool with five parameters and a complex return value.

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 has five parameters, no schema descriptions, and no annotations, the description is remarkably complete. It covers all parameters, describes the detection methods, and details the output dictionary. This is sufficient for an agent to understand what the tool does, when to use it, and what it returns.

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%, but the description fully compensates with an 'Args' section that provides a one-line meaning for each parameter: index, field, timeframe, sensitivity, and method (including allowed values for method and sensitivity). This adds substantial meaning beyond the input schema's bare property names and defaults.

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 statistical anomalies in time-series data' with specific detail about identifying 'unusual patterns, spikes, or outliers in security data that may indicate threats.' This distinguishes it from sibling tools like 'identify_rare_events' and 'baseline_normal_behavior' by focusing on anomaly detection in time-series security data.

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 usage for threat detection in security data ('may indicate threats') but does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. The sibling tools include closely related ones like 'identify_rare_events' and 'baseline_normal_behavior,' so clearer differentiation would improve guidance.

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