variance

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

With no annotations, the description carries the full burden, and it does disclose the success and error response shapes, which is genuinely useful. It omits important behavioral details for a statistics tool: whether this is sample or population variance, and what happens for an empty list or non-numeric input.

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 purpose is front-loaded in the first sentence, and the Args/Returns/Examples block is compact and skimmable. The boilerplate section headers are slightly heavy for a one-parameter tool but nothing is wasted.

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?

There is no output schema, but the description compensates by documenting the return payload for both success and error cases. The only meaningful gap is the undefined sample-vs-population semantics, which an agent needs in order to trust the result.

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%, but the single parameter is documented in the description as 'A list of numerical values,' which adds meaning beyond the bare array-of-number schema. With only one self-evident parameter, this is adequate.

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 verb and resource ('Computes the variance of a list of numbers'), which is unambiguous. It does not, however, explicitly differentiate itself from close siblings such as standard_deviation, mean, or median, leaving that inference to the agent.

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 and no mention of alternatives, even though standard_deviation and mean are obvious near-neighbors in the sibling list. The worked example implies basic invocation but provides no selection criteria.

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