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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior. The description adds useful context by stating the per-call price, x402/Solana payment context, and HTTPS endpoint, though it does not describe the return value format beyond examples.

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 computation, then examples, range, and payment/endpoint details. Every sentence adds value for a simple one-parameter math tool.

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 simple one-parameter tool with no output schema, the description is complete enough: it defines the operation, gives the valid n range, provides sample outputs, and discloses payment context. An agent has what it needs to call it correctly.

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 coverage is 0%, so the description must carry parameter meaning. It does so by stating that n runs from 0 to 12 and giving concrete example outputs for specific n values, which clarifies the parameter beyond the schema's bare minimum/maximum constraints.

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 exactly what is computed: the sum of Domb numbers D(0) through D(n). The examples and range make it distinguishable from sibling tools such as domb, domb_term, domb_mod, and domb_gcd.

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 description implies this is for cumulative sums through n, but it does not explicitly say when to use this instead of the individual-term sibling tools. The range and examples help, but there are no named alternatives or exclusions.

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