get_promotion_analysis

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

No annotations are provided, so the description carries full burden. It indicates a read operation (analysis) and lists the metrics (usage counts, discount types, status distribution), but does not disclose whether the tool returns aggregated data, if pagination is involved, or any authentication/permission requirements. This is adequate but leaves gaps.

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 extremely concise: two short sentences in Chinese, front-loaded with the main purpose. Every word earns its place 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?

For a simple analysis tool with 2 parameters and no output schema, the description covers the core purpose and output metrics. It could describe the return format (e.g., list of aggregated records) but is largely complete given the tool's simplicity.

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?

Input schema coverage is 100% (both parameters described). The description does not add new meaning to the parameters beyond what the schema already provides. Baseline score of 3 is appropriate as the schema already handles parameter documentation.

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: analyzing promotion campaign effects including usage counts, discount types, and status distribution. It distinguishes itself from sibling tools like get_promotion_roi (specific ROI analysis) and get_promotion_detail (single promotion detail) by focusing on aggregated 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 implies use for analyzing promotion effects and mentions combining with sales data for ROI, but does not explicitly state when to use this tool over alternatives like search_promotions or get_promotion_roi. No usage exclusions or prerequisites are provided.

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