recommend

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

With no annotations provided, the description carries the behavioral burden. It usefully discloses default current-time usage, randomness semantics (0=regular, 1=surprise), count limits, and mood options, which is meaningful. However, it does not state whether the recommendation uses inventory data, whether it has side effects, or what constraints apply beyond the parameters.

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 one purpose line plus a tight parameter list with no filler. The primary function is front-loaded, and each bullet adds distinct, useful information.

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 description covers all parameters and the high-level return shape, and an output schema exists to handle return details. It is slightly incomplete because it does not explicitly route between hot-brew recommend and coldbrew_recommend, nor state whether inventory is considered, but it remains highly usable.

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 0%, but the description compensates fully by documenting all four parameters: datetime_str ISO syntax and fallback, randomness range with default 0.6, count default and max 5, and mood examples. This gives an agent everything needed to form correct arguments.

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 opening line states a specific verb ('推荐') and a concrete resource: what tea to drink today or at a given datetime, plus the exact output shape (400ml 壶泡一泡的完整参数). The '壶泡' detail implicitly differentiates it from the sibling coldbrew_recommend, so an agent can tell them apart.

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?

No explicit when-to-use or when-not-to-use guidance is given, and no sibling alternative such as coldbrew_recommend is named. The '壶泡' wording implies hot-brew usage and the datetime parameter implies current-time behavior, but the choice is left to inference rather than spelled out.

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