detect_anomalies

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

With no annotations, the description carries full burden. It discloses the statistical methods (IQR and Z-score) and the types of anomalies flagged. It does not discuss auth needs, rate limits, or side effects, but for a read-only analysis tool, this is adequate.

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 concise with two sentences, no redundant information, and the purpose is front-loaded. Every sentence adds 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 no output schema and no annotations, the description adequately explains the tool's function, method, and scope. It covers the main purpose and usage examples, though it could mention what the output looks like (e.g., list of anomalies).

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 explains the parameters (days and metrics). The description adds context by mentioning 'health data' and outlier detection, but does not significantly extend beyond the schema.

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 detects unusual readings in health data over a time period, specifying the verb 'detect' and the resource 'health data'. It also mentions the statistical methods used (IQR and Z-score), which distinguishes it from sibling tools like analyze_sleep_quality or correlate_metrics that perform other types of analysis.

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 good context on when to use the tool, such as identifying nights with poor sleep or stress spikes. However, it does not explicitly state when NOT to use it or mention alternative tools, though the examples imply its use case.

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