qlty_standardDeviation

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

Annotations already declare readOnlyHint and idempotentHint, so the tool is read-only and idempotent. Description adds that it calculates mean and stddev, and explains the persist argument (materializes result as volatile table). No contradictions.

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 clear sentences followed by a concise argument list. Front-loaded purpose, no wasted words. Every sentence earns its place.

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 tool with annotations and no output schema, description covers behavior, usage, parameters, and alternatives. Complete and sufficient for agent to select and invoke correctly.

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% with parameter descriptions. Description repeats parameter info, adding no new semantic meaning beyond what schema provides. Baseline 3 is appropriate.

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?

Description states it calculates mean and standard deviation for a single numeric column, with specific verb 'Calculate'. It distinguishes from sibling tool qlty_univariateStatistics, which provides a fuller profile.

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?

Explicitly says when to use: 'when the user asks specifically for standard deviation, the spread of values, or just mean and variability.' Also provides alternative: 'For a fuller statistical profile... use qlty_univariateStatistics instead.'

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