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

The annotations already declare the operation read-only, idempotent, open-world, and non-destructive. Beyond that, the description adds important behavioral context: rejected inputs, the 600-digit limit, and a payment requirement of $0.001 USDC per successful call via x402 on Solana, plus an HTTPS alternative endpoint.

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 operation and uses compact sentences that each serve a purpose: indexing examples, rejection behavior, input size, pricing, and the HTTPS fallback. There is no redundant or filler text.

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?

Given the single required parameter, no output schema, and annotations that already cover safety properties, the description supplies enough context to call the tool correctly. It covers the return concept, input constraints, failure cases, payment model, and alternate endpoint.

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 carry parameter meaning. It clarifies that value is a decimal integer of at most 600 digits and that non-Leonardo numbers or leading-zero inputs are rejected, adding semantic validation beyond the raw pattern and maxLength fields.

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 clearly states a specific operation: return the smallest index n such that L(n) equals the supplied value. It is distinguishable from sibling sequence tools like leonardo, leonardo_sum, and leonardo_mod because it performs the inverse/index lookup rather than generating terms or sums, though it does not name those siblings explicitly.

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 inverse relationship: use this when you already have a Leonardo number and need its index. The description gives rejection conditions for non-Leonardo numbers and leading zeros, but it does not explicitly say when to choose this over sibling tools such as leonardo or leonardo_sum.

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