biz_anomaly_scan

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 computation windows, metric definitions, and warns against fabricating internal docs, but it does not explicitly state that the scan is read-only, nor describe permissions, side effects, or output behavior.

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 uses compact labeled sections and front-loads the core purpose before metric details and usage guidance. Every sentence adds distinct information with no 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?

With an output schema present, return values need not be described. Metric semantics, date windows, and cross-tool guidance make the tool callable; the only meaningful gap is the under-specified optional end_date parameter.

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 meaningfully defines the three metric values with exact comparison windows. However, it does not explicitly explain the end_date parameter's format, default meaning, or how it interacts with the metric windows, so it stops short of a 5.

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 first line states it performs period-over-period anomaly scans across GMV, refund rate, and ad conversion. This gives a specific verb, resource, and clear scope that distinguishes it from sibling data-query tools.

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 【何时用】 section explicitly lists triggering user intents like '有什么异常' and '退货率是不是高了'. It lacks explicitly named alternatives or when-not-to-use conditions, but the context is clear enough for routing.

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