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

Annotations already establish readOnly, non-destructive and closed-world, so safety is covered. The description adds genuinely useful behavior: theme mode returns ten names, word mode produces club-style constructions. It does not mention randomness or whether suggestions repeat, but for a pure generation tool this is solid added context.

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 with zero filler; both output modes are stated economically and nothing needs trimming.

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?

There is no output schema, so the description must carry return-shape information, and it does indicate what the caller gets (ten themed names, or club-style names). Missing details such as response structure or whether results are deduplicated are minor for a low-risk generator with covered annotations.

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 description coverage is only 50% (the theme enum is undocumented in the schema). The description compensates by explaining that theme yields ten names and that word is used to build club-style names from something like a town or pub, which gives the agent enough to pick the right parameter.

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 (suggest) and resource (football team names), plus two distinct output modes: themed lists and club-style names derived from a word. It is clearly different in kind from sibling mutation/selection tools like make_fair_teams or pick_teams, though it never names a sibling to sharpen the contrast.

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 phrase 'ten from a theme, or club-style names made from a word' implicitly tells the agent which input produces which result, so the mode selection is inferable. However, there is no explicit statement of when to choose this tool over name-producing alternatives, and no guidance on what happens if both or neither parameter is supplied.

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