get_revenue_anomalies

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

No annotations are provided, so the description carries the full burden. It discloses the return format (JSON string with date, amount, expected, deviation, type) and the empty-list behavior, but it does not mention permissions, data freshness, or what 'unusual' means algorithmically. There is no contradiction with annotations.

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 one-sentence purpose, an Args block, and a Returns block. It is concise and front-loaded, with no wasted words.

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 tool is simple with two optional parameters and an output schema. The description covers purpose, parameters, and returns, making it self-contained. It could add more context about anomaly types, but it is not necessary for a basic understanding.

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. It explains app_id as 'Filter by app (optional)' and lookback_days as 'Analysis window in days (default 90)', adding meaning beyond the schema's type/default. Both parameters are addressed.

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 opens with 'Detect unusual patterns in revenue', which is a specific verb and resource. It clearly distinguishes itself from siblings like get_revenue_summary and get_revenue_forecast by focusing on anomaly detection.

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 when to use this tool (when revenue anomalies are of interest) but provides no explicit exclusion or comparison with alternatives. It does not mention, for example, 'use this to identify outliers rather than forecast trends'.

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