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

No annotations are provided, so the description carries the full burden. It adds context about the output behavior (identifies winners by protein and calorie density) but does not explicitly disclose read-only nature, error handling, or return format. Some value is added, but gaps remain.

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 two concise, front-loaded sentences with no wasted words. It efficiently states the primary function and a key behavioral outcome.

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 one-parameter tool, the description covers the essential purpose and output (identifying winners). It could be more explicit about the return format or prerequisites (e.g., needing at least two IDs), but given the simplicity and clear schema, it is adequately 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% (the 'ids' parameter is fully described). The description does not add any additional meaning beyond what the schema already provides, so the baseline score 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 ('Compare') and resource ('nutrition profiles of multiple foods'), clearly distinguishing it from siblings like get_nutrition (single food) and search_food (search). It also highlights the unique outcome of identifying winners by protein and calorie density.

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 context is clear: use this tool when you want to compare multiple foods side-by-side. However, it does not explicitly state when not to use it or name alternatives, leaving the distinction to inference.

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