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

Annotations already establish read-only, idempotent, open-world behavior, and the description adds meaningful operational context: price per successful call, x402/Solana payment, and an HTTPS endpoint alternative. It does not clarify return format, but it substantially enriches the behavioral picture beyond annotations.

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 definition and examples, then appends practical details about range and payment. All sentences are relevant, though the input range duplicates schema bounds.

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 compute tool with no output schema, the description defines the mathematical result but leaves the exact return format ambiguous — e.g. whether it returns Fibonacci indices, a bit string, or a rendered expression. Payment and endpoint details are helpful, but the output shape should be clearer.

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?

The single parameter n has 0% schema description coverage, and the description mostly restates its schema bounds (1 to 1000000000000). It does anchor n mathematically as the number whose Zeckendorf representation is returned, which is useful but does not fully compensate for the missing schema explanation.

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 names a specific mathematical object and defines it precisely with Fibonacci indexing and worked examples. It is clearly about computing the Zeckendorf representation of n, but it does not explicitly distinguish this tool from siblings like zeckendorf_bits or zeckendorf_sum.

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?

It supplies the mathematical definition and input range but gives no explicit guidance on when to use this tool versus its siblings. The usage is implied by the definition rather than stated, and there are no exclusion conditions.

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