percentage_calc

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

With no annotations provided, the description carries the full behavioral disclosure burden. It does explain the semantics of each operation (e.g., percent_of, tip, discount), which is helpful. However, it does not disclose return format, rounding behavior, edge cases like zero values, or error handling, which a calculator tool would benefit from stating.

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 compact sentence that front-loads the tool's purpose and then concisely lists every operation with its relevant inputs. There is no filler or repetition, and it efficiently conveys a lot of operational detail in a small space.

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?

The description covers all operations and parameter mappings, and the schema provides rich field descriptions, so the main calling contract is clear. However, there is no output schema and the description does not mention what the tool returns (e.g., a single number, a per-person split amount, or an object), which is a notable gap for an agent executing the tool.

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 100%, so the baseline is already 3. The description adds meaningful operational context by mapping parameters to each operation, such as 'tip (bill + tip% + optional split)' and 'percentage_change (old to new)', clarifying which parameters apply to which calculation. This goes beyond the individual field descriptions.

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 states the tool performs common percentage calculations and enumerates six specific operations with their formulas. It is unambiguous about what the tool does, but it does not explicitly distinguish itself from sibling tools like calculate or statistics.

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 usage by listing the supported operations and their parameter mappings, so an agent can infer when to use it for percentage tasks. However, it gives no explicit guidance about when not to use it or when a sibling tool like calculate or statistics would be more appropriate.

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