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

Annotations already cover the safe-read profile (readOnly, idempotent, non-destructive), yet the description adds genuinely new facts: the cost ($0.001 USDC per successful call, x402 on Solana) and an equivalent HTTPS endpoint. It also enumerates the possible output classes, which is the key behavioral disclosure for a classifier.

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?

Purpose is front-loaded in the first sentence, followed by compact examples and then the constraint and pricing. Every sentence carries information; the only slight imbalance is that pricing/endpoint detail takes as much space as the actual function.

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, no-output-schema tool this is nearly sufficient: the callable domain, the output classification set, and the commercial terms are all stated. The main omission is behavior at the upper bound or on invalid input, which is minor for a pure read.

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 has to carry the parameter meaning and it partially does: it establishes that n is the integer being classified and that it runs from 1 to 1000000000000. It never says n must be an integer, and the stated range merely repeats the schema's min/max rather than adding semantics.

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?

"Classify n from its divisor sum" states a specific verb and resource, and the three output classes (perfect, abundant, deficient) pin down exactly what the result is. It is distinguishable from value-returning siblings like abundance or divisor_sum, though it never names them 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?

The worked examples (6, 28 perfect; 12 abundant; 8 deficient) imply when to use it, but there's no explicit when-to-use/when-not-to-use guidance and no pointer to related tools such as abundance or aliquot. With hundreds of number-theory siblings, a routing sentence would have raised this.

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