variance

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool calculates without any information about how it behaves: no mention of error handling (e.g., for empty arrays), computational complexity, precision, or what the output format looks like. This is inadequate for a tool with parameters.

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 extremely concise - a single phrase that directly states the tool's purpose. There's zero wasted language or unnecessary elaboration. While it's arguably too brief for completeness, as a standalone statement it's perfectly 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 tool has 2 parameters with 0% schema coverage, no annotations, and no output schema, the description is completely inadequate. It doesn't explain what the tool returns, how to interpret the 'sample' parameter, what format the 'numbers' array should be in, or any behavioral aspects. For a statistical calculation tool with parameters, this minimal description leaves critical gaps.

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%, meaning neither parameter ('numbers' and 'sample') has descriptions in the schema. The tool description provides no information about parameters whatsoever - it doesn't mention that 'numbers' is an array of values or that 'sample' is a boolean flag for sample vs population variance calculation. The description fails to compensate for the complete lack of schema documentation.

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 '计算数组的方差' (calculate the variance of an array) clearly states the verb ('计算' - calculate) and resource ('数组的方差' - variance of an array). It distinguishes this tool from siblings like 'mean', 'stdDev', and 'sum' which perform different statistical operations. However, it doesn't explicitly differentiate from 'stdDev' (standard deviation) which is mathematically related to variance.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when variance is preferred over standard deviation ('stdDev') or other statistical measures like 'mean' or 'range'. There's no context about appropriate use cases, prerequisites, or exclusions.

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