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

Annotations already declare readOnlyHint and idempotentHint, so no need to restate safety. The description adds the important n-1 denominator detail (sample vs population) and the percent-to-volatility scaling behavior, which go beyond the schema and annotations.

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?

Two short sentences, front-loaded with the core function and a practical formatting hint. No wasted words, every phrase adds value.

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?

For a simple one-parameter read-only tool with an output schema, the description covers all necessary behavioral details: what stats are computed, the n-1 adjustment, and the input-format effect. No gaps remain for effective selection and invocation.

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 100% for the only parameter, providing a basic description. The description adds meaning by explaining that passing percents yields a percent stdev, clarifying how input values are interpreted and how output scales, which enriches the parameter semantics.

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 computes mean, sample variance, and sample standard deviation (with n-1 explicitly noted) for a series of returns. This distinguishes it from financial calculation siblings by focusing on descriptive statistics of returns.

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 implies usage on a return series and gives input guidance ('Pass percents to get a percent stdev'), but it does not explicitly contrast with alternatives or state when not to use it. The context is clear but exclusions/alternatives are missing.

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