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

Annotations already establish read-only and idempotent behavior, and the description adds meaningful context beyond them: unorderable amounts are rejected, n is bounded to 0–5000, and the call costs $0.001 USDC via x402 on Solana. It does not contradict the annotations, though it omits details about the exact rejection behavior or return payload.

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 front-loaded with the core computation, then adds examples, rejection behavior, range, price, and an alternate endpoint. Every sentence contributes useful information without redundancy.

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 one-parameter computational tool with no output schema, the description provides enough to call it correctly: target range, examples, rejection of impossible amounts, and payment context. It could be slightly more explicit about the return value, but the examples make the output shape reasonably clear.

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 compensate. It defines n as an amount in the range 0–5000 and provides worked examples showing how specific values map to box counts, which adds semantic meaning beyond the bare integer schema.

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 computation: the fewest boxes of sizes 6, 9, and 20 that sum to n. The phrase 'fewest boxes' distinguishes it from sibling tools like mcnugget_ways or mcnugget_count, but the description does not explicitly name or contrast those alternatives.

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 implied by the worked examples and the domain-specific problem, but there is no explicit guidance on when to use this tool versus mcnugget, mcnugget_count, mcnugget_miss, or mcnugget_ways. The agent must infer the selection from the name and phrasing.

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