get_promotion_analysis

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It describes the analysis outputs (usage counts, discount type, status distribution) which implies a read-only aggregation behavior. However, it does not explicitly state safety, response format, pagination, or any side effects. Given the absence of annotations, this is a partial but not complete disclosure.

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 two concise sentences, front-loaded with the primary purpose and specific output details. Every sentence adds value, and there is no superfluous text. It is well-structured for quick scanning.

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 analysis tool with two optional filters and no output schema, the description communicates the key outputs (usage count, discount type, status distribution) and suggests a use case (ROI evaluation with sales data). It lacks explicit return format or pagination details, but given the tool's simplicity, it is fairly complete. The mention of ROI could be clearer, given the sibling tool get_promotion_roi exists.

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 100%: both 'status' and 'discount_type' have descriptions in the schema. The description mentions discount type and status as part of the analysis output but does not add extra meaning to the parameter semantics beyond what the schema already provides. Baseline 3 is appropriate as the schema carries the load.

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 analyzes promotion effectiveness, listing specific outputs: usage count, discount type, status distribution. It uses the specific verb '分析' (analyze) and resource '促銷活動' (promotions), and the scope is differentiated from sibling tools like list_promotions by focusing on aggregated analysis rather than listing or detail. However, it does not explicitly name sibling tools or contrast itself, so it loses a point.

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 some usage context: '可搭配銷售數據評估促銷 ROI' indicates it can be used with sales data to evaluate ROI, implying a complementary role. However, it does not explicitly state when to prefer this tool over alternatives like get_promotion_roi or list_promotions, nor does it mention any exclusions. The guidance is implied rather than explicit.

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