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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior. The description adds useful non-annotation context: the $0.001 USDC x402 price per successful call and an HTTPS endpoint alternative, which matters for an agent deciding how to invoke it.

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 definition is front-loaded with the core predicate, then examples, range, payment details, and endpoint. Every sentence is short and useful, with no wasted preamble.

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 predicate with rich safety annotations and no output schema, the description is nearly complete. It could explicitly state the return value is boolean and mention the count-based sibling, but the core call semantics are 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 carry the parameter burden. It defines n as the integer being tested and states the 0–100000 range with examples, though it repeats the schema's min/max rather than adding deeper format guidance.

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 precise predicate: whether n has at least two different writings as a sum of two positive cubes. Examples (1729, 4104, 91, 100) make the operation concrete and distinguish it from a mere representation-counting tool like taxicab_count.

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 gives examples and the domain range 0–100000, but it never says when to use this predicate instead of taxicab_count, two_cubes, cube_ways, or other representation-related siblings. The agent must infer the use case from the mathematical definition alone.

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