Summarize a list

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

With no annotations, the description carries the full disclosure burden. It helpfully enumerates the computed outputs and flags a scale constraint ('small list'), but it does not clarify the 'population/sample standard deviations' ambiguity (both computed? which flag?), nor return shape, ordering, or behavior on edge inputs.

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?

A single front-loaded sentence that states the operation and every computed statistic with zero filler. Nothing is wasted.

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?

No output schema exists, so the description's enumeration of computed statistics partially covers the return value. However, it leaves gaps on parameter bounds, the population-vs-sample ambiguity, and numeric formatting/edge cases that an agent would want before calling.

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?

One parameter at 0% schema description coverage. The description only vaguely gestures at the constraint via 'small list', without stating the 1–1000 item bounds or that values must be numbers, so it fails to compensate for the undocumented schema.

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 names a specific verb ('compute') and enumerates the exact statistics produced (mean, median, range, population/sample standard deviations) over a list. It is unambiguous about what the tool does. It doesn't differentiate from siblings, but the siblings (quadratic_report, get_official_links) are unrelated enough that differentiation adds little.

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?

There is no explicit guidance on when to use this tool or when not to. The phrase 'for a small list' hints at an intended scale but gives no threshold or exclusions, leaving usage context to inference.

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