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. It mentions what the tool does (calculates variance) and the conceptual measure, but it does not describe key behavioral traits such as input validation (e.g., handling non-numeric strings in the 'numbers' array), error handling, output format, or performance considerations (e.g., computational complexity for large lists). This is a significant gap for a tool with no annotation coverage.

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 concise and front-loaded: the first sentence directly states the tool's purpose, followed by a brief explanation and domain tags. There is no wasted verbiage, and it efficiently conveys the core idea in a single sentence with additional context. However, it could be slightly improved by integrating parameter hints without sacrificing brevity.

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's complexity (statistical calculation with two parameters), lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It does not address parameter details, behavioral expectations, or output information, leaving significant gaps for an AI agent to understand how to correctly invoke and interpret results from this tool.

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 the schema provides no descriptions for the parameters. The description does not compensate by explaining the parameters: it does not mention the 'numbers' array (e.g., expected format, numeric conversion) or the 'population' boolean (e.g., meaning of true/false for population vs sample variance). With two parameters and no schema descriptions, the description adds minimal semantic value beyond the tool's general purpose.

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's purpose: 'Calculate the sample variance of a list of numbers. Measures how spread out the numbers are from the mean.' It specifies the verb ('calculate'), resource ('sample variance'), and provides a conceptual explanation. However, it does not explicitly differentiate from sibling tools like 'standard_deviation' or 'covariance', which are related statistical measures, so it falls short of a perfect score.

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 includes a domain/category tag ('Domain: statistics, Category: general'), which implies usage in statistical contexts, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'population' vs 'sample' variance, or when to choose variance over standard deviation). No when-not-to-use or prerequisite information is given, leaving usage largely implicit.

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