recommend_food_pairings

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

With no annotations and no output schema, the description carries the full burden of behavioral disclosure. It only says the tool ranks pairings and offers a parameter fallback hint; it does not disclose whether the operation is read-only, what happens when a beverage/brand cannot be matched, how the ranking is ordered, or what the response looks like.

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 concise sentence with the core action front-loaded and a usage hint added neatly after the semicolon. It avoids filler, though the phrase 'category/variety' introduces some ambiguity that a slightly more explicit phrasing could remove.

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?

Given five parameters, no output schema, no annotations, and zero schema description coverage, this short description is not sufficient for an agent to reliably construct a correct call. The agent would not know the required beverage_name semantics, how optional parameters interact, or what result format to expect.

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 0%, so the description must explain the parameters. The only guidance is 'pass category/variety for unknown brands,' which is ambiguous because there is no explicit category parameter and the mapping to variant and food_category is unclear. It does not explain beverage_name, brand, limit, or food_category semantics.

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 states a specific action (rank) and resource (pairings derived from beverage characteristics), which aligns with the tool name and distinguishes it from sibling cocktail-recommendation tools. It could be slightly more explicit that the pairings are food pairings, but the intent is reasonably clear.

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 one useful usage hint: pass category/variety for unknown brands. However, it does not explain when to choose this tool over sibling tools like recommend_cocktails_from_inventory, nor does it mention any exclusions or prerequisites. The guidance is present but minimal.

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