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

No annotations are provided and the description is purely a label. There is no disclosure of input expectations, edge cases, return format, or any behavioral trait.

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 text is short but this is under-specification, not effective conciseness. It contains no informative content beyond the tool name and wastes the only opportunity to clarify the tool's purpose.

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 statistical tool with no output schema and no annotations, the description is entirely inadequate. An agent has no idea what inputs are processed, what result to expect, or how this relates to sibling statistical tools.

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?

With zero parameters in the schema, there is nothing for the description to explain. The schema already conveys that no arguments are needed, and the description cannot add meaning beyond that.

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 'Coeff Variation: Coeff Variation' is a tautology that merely restates the name. It provides no verb, resource, or explanation of what the tool computes.

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?

No guidance is given on when to use this tool versus the hundreds of sibling tools. The agent cannot infer when coefficient of variation is needed or when a related statistics tool (e.g., variance, stddev, get_stats) would be appropriate.

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