recommend_drinks

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does convey the core behavior: recommendations are based on user preferences. Still, it does not mention whether this is a read-only tool, whether it respects all constraints, or what happens when no matching drinks exist, which are relevant 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 a single front-loaded sentence with no filler or redundancy. Every word contributes to identifying the tool's purpose and preference-based behavior, making it appropriately concise.

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 read-only recommendation tool with fully documented optional parameters, the description is minimally sufficient for invocation. However, it lacks explicit guidance on when to prefer it over get_menu, does not describe the output format, and has no annotation support, so it is not fully complete.

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 parameters like mood, count, max_price, and caffeine_free are already well documented with defaults and meaning. The description only adds the general 'based on preferences' idea and does not deepen parameter understanding, so the baseline of 3 is appropriate.

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 uses a specific verb and resource: '根據偏好推薦侘茶飲品' clearly means 'recommend wabi-cha drinks based on preferences.' It is distinct from the sibling tools such as get_menu, create_order, or cancel_order, because it is explicitly about preference-driven recommendations rather than menu retrieval or order management.

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?

Usage context is implied: the tool is for recommending drinks when the user expresses a preference like '想喝清爽的' or '不要咖啡因'. However, the description does not explicitly state when not to use it or name alternatives like get_menu for browsing the full menu, so the routing decision is left to inference.

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