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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the marginal bar is what behavior is added. The description contributes the data source (USDA measured cooking yield) and discloses that uncertainty is exposed via USDA high/low end values, which is genuinely useful. It does not say what happens when the food or method is unrecognized, nor which method is assumed if omitted.

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?

Three sentences ordered purpose, then usage, then return values, all of which earn their place. The first sentence is a noun-phrase fragment rather than a verb-led statement, a minor stylistic inefficiency but not a comprehension problem.

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?

With five parameters, a single required input, and no output schema, the description correctly takes on the return-value burden by naming the four units and the high/low yield bounds. The remaining gap is failure and fallback behavior for unknown or mismatched food/method combinations, which matters for a lookup-driven conversion.

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% and every parameter including the yield_id override is fully documented in the schema, so the baseline of 3 applies. The description adds no parameter-level detail such as default unit handling or how food/method interact when both are supplied.

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 clause names the concrete transformation (raw weight a cooked portion came from) and the domain (meat or poultry), so the direction is unambiguous against the sibling convert_raw_to_cooked. It never names that sibling explicitly, so an agent must infer the inverse relationship from the verb alone rather than from stated contrast.

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?

Two concrete usage contexts are given: logging a cooked portion against raw nutrition data, and scaling a recipe written in cooked weight. That is real when-to-use guidance rather than implied usage. What is missing is any exclusion or pointer to alternatives, e.g. when to use convert_raw_to_cooked instead or when to bypass food/method in favor of yield_id.

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