get_food

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

With no annotations, the description carries the full burden and does an excellent job. It discloses case-insensitive matching, tolerant matching rules, the ambiguity policy for multiple matches, the exact return format, and the no-match error format with suggestions. It even instructs the caller to retry with a suggested name verbatim. This is far beyond typical description depth.

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 well-organized with a one-sentence summary, a short matching-behavior paragraph, an Args section, and a Returns section. Every sentence adds useful information—no filler or repetition. The length is justified by the important edge-case behavior that must be communicated.

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?

Despite having an output schema available, the description still clarifies return semantics and the error structure, which is helpful for planning. The matching rules, ambiguous-name handling, and examples make the tool self-contained. For a one-parameter lookup tool, this is complete and leaves minimal ambiguity.

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 only defines 'name' as a string with no description, so the description must compensate and does so thoroughly. It explains what the parameter means ('The food name'), provides concrete examples including a formatted one ('greek yogurt (0% fat)'), and clarifies matching tolerance. This gives the agent everything needed to construct valid invocations.

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 opens with a specific verb and resource: 'Look up a single food by name and return its full entry with derived calories.' This clearly distinguishes the tool from siblings like list_foods (listing many) and calculate_macros (computing macros separately). It leaves no doubt about the tool's core function.

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 clearly conveys when to use this tool: when you need one food's details by name. It does not explicitly mention alternative tools or when not to use it, but the 'single food' framing and the listed sibling names provide enough context for a competent agent. A direct alternative comparison would lift this to 5.

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