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

Annotations already establish the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, and the description still adds real substance: the response payload contents (yield range, sample count, moisture and fat change) and the presence of alternative rows carrying yield_id. What it omits is matching behavior — whether the food/method lookup is fuzzy, and what happens on zero matches.

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?

A single dense sentence, front-loaded on the primary lookup and then the returned fields, with no filler clauses. It is packed rather than padded, though splitting the return-content portion into its own sentence would improve scanability.

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 no output schema, the description carries the burden of describing results and does so adequately by listing every returned field and the alternative rows. The remaining gap is the resolution story — how closely the supplied food/method must match and what a miss looks like.

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%, so the schema already documents food, method (with example values) and limit; the description adds nothing about syntax or accepted forms. The only extra signal is the reference to alternative rows and yield_id, which weakly justifies the limit parameter but doesn't explain its trade-offs.

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 gives a specific verb ('look up') and resource ('USDA cooking yield percent') scoped to a cut plus a cooking method, and enumerates what comes back (range, sample count, moisture/fat change, alternatives). It is clearly a targeted lookup rather than the bulk enumeration implied by list_cooking_yields, though it never names the siblings to sharpen that boundary.

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?

Usage is only implied: an agent can infer this is the tool to use when it has a specific food + method and wants the USDA yield figure, and the mention of 'up to 4 close alternative rows' hints at disambiguation. There is no explicit when-to-use statement, no exclusion of the conversion/calculation siblings, and no guidance on what to do when the food or method doesn't match.

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