variance

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

Annotations already indicate read-only, idempotent behavior. The description adds that the calculation can be sample or population variance. However, it does not disclose edge cases (e.g., empty arrays) or the exact formula, which is beyond annotations' scope.

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 two sentences, directly stating the purpose and the key parameter toggle. No extraneous information; it is front-loaded and efficient.

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?

Given the low complexity and no output schema, the description covers the main functionality. It lacks explicit mention of return type, but for a calculation tool, the return is a number. The context is mostly complete.

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?

The description explains the 'population' parameter meaning. The 'numbers' parameter is implied but not explicitly defined. With 0% schema coverage, the description partially compensates but leaves room for ambiguity on the array structure.

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 computes variance, differentiating between sample variance (n-1) and population variance (n) via the population parameter. It uses the verb 'variance' implicitly, and identifies the resource as a set of numbers. Among sibling tools like std_dev and avg, this is distinct.

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 explains when to use sample vs. population variance, guiding the agent on setting the population boolean. It does not explicitly mention alternatives like std_dev or when not to use, but the guidance is clear for its primary use case.

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