percentage_of

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds the formula and example, but does not disclose any additional behavioral traits (e.g., handling of negative numbers, precision, or error cases). With rich annotations, this is adequate.

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 a single sentence with an example, containing no wasted words. It front-loads the core purpose and is optimally concise for the tool's simplicity.

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 basic mathematical tool with no output schema, the description covers the essential purpose and parameter roles. Annotations handle safety. It is nearly complete, though it could optionally mention return type or edge cases, but these are not critical.

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 parameters have no embedded descriptions. The description fully compensates by explaining that 'percent' is the percentage value and 'total' is the base number, as illustrated in the example. This makes the parameter semantics unambiguous.

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 specifies that the tool computes a percentage of a total, with a concrete example (15% of 200 = 30). This immediately distinguishes it from sibling tools like percentage_change or percentage_reverse, which perform different operations.

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 implicitly indicates usage for simple percentage calculations, but does not explicitly state when to use it versus alternatives, nor does it mention any exclusions or prerequisites. Given the simplicity, some guidance is implied but not formally provided.

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