recommend_coffee

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

No annotations are provided, so the description must disclose behavior on its own. It only states the tool's purpose and parameter meanings, without describing the return format, error handling, or underlying logic. The output of the recommendation (e.g., a coffee name, a ranked list) is not disclosed.

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 sentence plus a compact argument list, with no redundant text. Each line adds specific parameter guidance, and the structure is scannable.

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 tool is simple with four optional parameters, but there is no output schema and no annotations. The description does not specify what the recommendation returns or any behavioral constraints, leaving a moderate completeness gap given the lack of structured context.

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?

The schema has 0% description coverage, but the description compensates by explaining each parameter's meaning and allowed values (e.g., 'True for milk drinks, False for black coffee, None for either', 'low, medium, or high' for caffeine). This adds significant value beyond the raw schema.

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 'Recommend a coffee from mood and simple preferences' – a specific verb+resource and clarifies the input basis. This distinguishes it from sibling tools like list_coffee_menu and explain_recommendation.

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 usage when a user has mood and preferences, but it does not explicitly address when to prefer this tool over list_coffee_menu or explain_recommendation. There is no 'when not to use' guidance, so it falls at implied usage.

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