variance_calc

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is covered by structured data rather than prose. The description adds value only by listing the returned metrics (partially compensating for the missing output schema); it says nothing about how population vs. sample is chosen or what happens with malformed 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?

One short sentence with zero waste, front-loading the verb and the output set. Nothing to trim and nothing buried.

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?

Since there is no output schema, the enumeration of returned statistics is the right thing to include, and it is present. But the description says 'population/sample variance' while the schema exposes no mode selector, leaving the agent unsure whether both are returned or which one applies; input validation behavior is also unaddressed.

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 single 'values' parameter is already documented including the at-least-2 constraint. The description never mentions the input shape at all, so it adds nothing beyond the schema — baseline 3 applies.

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 names a specific verb ('Calculate') and enumerates the exact statistics produced (variance, standard deviation, median, range, coefficient of variation), so the agent knows precisely what comes back. It does not, however, distinguish this from heavily overlapping siblings such as descriptive_stats, statistics_calc, or percentile_calc, which likely cover much of the same ground.

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 guidance on when to choose this tool over descriptive_stats/statistics_calc, and no prerequisites or exclusions are stated. The only implied usage is 'you have an array of numbers' — everything else is left to inference.

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