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

Annotations already declare readOnly, idempotent, non-destructive, open-world behavior, so the bar is lower, yet the description adds real context beyond them: a per-call price of $0.001 USDC via x402 on Solana and an equivalent HTTPS endpoint. It also discloses the output guarantee that the residue is smaller than the modulus, which is genuine behavioral information the annotations do not carry.

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 formula is front-loaded and the remaining sentences are short and each carries information (constraints, output bound, price, endpoint). No filler, though the sentence-per-fact style reads slightly like a field dump rather than prose.

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 does the necessary work by stating the return is a residue below the modulus, and it covers the two parameters, the domain limit, and the payment/transport model. An agent has enough to call it correctly and to know it incurs a cost; only a richer description of the return representation (exact string vs integer for large moduli) is absent.

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, and it does for 'mod': it clarifies that the value is a positive decimal integer and that zero is rejected, which matters because the schema's '^[0-9]{1,600}$' pattern would otherwise admit '0'. For 'n' it only restates the 0–4096 bounds already encoded as minimum/maximum, so the added value is uneven but present.

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 an explicit formula, 'L(n) mod m for the Leonardo sequence', which names the resource (Leonardo numbers), the operation (modular reduction), and the arguments. It implicitly separates itself from siblings like leonardo, leonardo_sum, and leonardo_index through the 'mod' operation, though it never names an alternative directly.

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 by the mathematical definition; there is no statement about when to pick this over leonardo, leonardo_fib, or the other Leonardo siblings. The constraints it does give (n from 0 to 4096, modulus positive, zero rejected) are input validity rules rather than guidance on tool selection.

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