math_power

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

No annotations are provided, so the description carries the full disclosure burden. For a pure, side-effect-free arithmetic function the safety profile is self-evident, but the description says nothing about edge behavior such as 0^0, negative bases with fractional exponents, or complex/NaN results.

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?

A single eight-word sentence with no filler, and the operation plus both operands are front-loaded. Nothing here wastes the agent's context.

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?

An output schema exists, so return values need not be described, and the two required numeric parameters are self-explanatory. The only real omission is edge-case behavior, which is minor for a pure math primitive.

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 is the only place parameter meaning could be clarified. It merely restates the parameter names ('base', 'exponent') without adding types, ranges, domain constraints, or units, so it does not compensate for the coverage gap.

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 specific operation (exponentiation) with both operands named, so the agent knows exactly what computation occurs. It does not, however, distinguish itself from math_multiply, which is the nearest sibling and a plausible alternative for repeated multiplication.

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?

There is no when-to-use guidance, no mention of when to prefer this over math_multiply or math_factorial, and no prerequisite or constraint information. The agent must infer usage from the name alone.

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