standard_deviation

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

With no annotations provided, the description carries the full burden. It discloses the non-empty precondition and the population-versus-sample choice, but it does not describe edge-case behavior such as what happens with an empty list, a single-element list, or non-finite numbers. For a simple numeric function this is average transparency.

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 a single, direct sentence with the key precondition ('non-empty') included. Every word earns its place, and the information is front-loaded for quick agent parsing.

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?

This is a simple single-parameter math tool, an output schema exists to describe the return value, and the description covers the essential metric and precondition. It could mention edge cases like empty or single-element lists, but for this complexity level it is reasonably 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?

Schema description coverage is 0%, so the description must compensate, but it only adds the 'non-empty list' constraint. It does not explain what values are valid, whether the list order matters, or how invalid inputs are handled. The schema already declares the parameter as an array of numbers, so the added semantic value is minimal.

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 ('Calculate'), a precise statistical quantity ('population standard deviation'), and the target data shape ('non-empty list'). This clearly distinguishes it from sibling tools like mean and median, and even distinguishes population standard deviation from a sample-based alternative.

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 phrase 'population standard deviation' makes the intended use unambiguous, and 'non-empty list' sets the applicability condition. It does not explicitly name alternatives or exclusions, but the sibling list contains no close alternative to this operation, so the context is sufficient.

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