standard_deviation

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

No annotations are provided, so the description carries the burden. It mentions a numerically stable algorithm, which is a positive behavioral trait. However, it does not disclose any side effects, authorization needs, or edge-case behavior. The description is adequate for a simple calculation tool but lacks detail.

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 sentence that is front-loaded with the verb 'Calculates', immediately conveying the action. Every part is purposeful with no wasted words.

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 it is a simple mathematical function with no output schema, the description is nearly complete. It specifies the two modes (population/sample) and mentions algorithmic stability. It could mention the return type (a number) but this is implicitly clear. Sibling tools are covered by distinct names.

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 coverage is 50% (only 'sample' has a description). The tool description does not add any additional meaning about the 'numbers' parameter beyond what the schema implicitly states. It falls short of compensating for the missing schema description for half the parameters.

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 it calculates population or sample standard deviation with a numerically stable algorithm. The name 'standard_deviation' matches exactly, and it distinguishes from sibling tools like variance, mean, etc. No ambiguity.

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 does not explicitly mention when to use this tool versus siblings, but the specific name and explanation of population/sample make the intended use very clear. A brief mention of alternatives would improve it, but it is not misleading.

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