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

With no annotations, the description carries the full burden. It usefully discloses the return fields (count, mean, std, histogram), but says nothing about edge cases like empty input, non-numeric values, or whether a histogram bin parameter exists, leaving behavioral gaps for a compute tool.

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?

Four short sentences, front-loaded with purpose, then input, output, and usage. No filler; each sentence carries distinct information, though the output enumeration makes it slightly long.

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 or annotations exist, so the description compensates by listing return values and input formats. It is nearly complete for a simple compute tool, with the main gap being the unexplained `locale` parameter and no error-handling notes.

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%. The description does add value by clarifying that `data` may be an array OR a space/comma-separated string, which the string-typed schema does not convey. However, the `locale` enum parameter is undocumented in both schema and description.

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?

States a specific verb ('describe') and resource ('data set') and then enumerates the exact outputs (count, sum, mean, median, std, min, max, quartiles, histogram). That makes the tool unambiguous, though it doesn't need to differentiate from the unrelated sibling utilities.

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 gives a trigger condition: 'Use when a user wants statistics on numbers.' No alternatives or exclusions are named, but no sibling overlaps in function, so this is adequate context without a routing problem.

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