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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description still earns credit by disclosing the returned shape (IDs, scope, descriptions, source URLs) and, more importantly, a non-obvious semantic caveat that country lists rank companies rather than countries — a point no structured field carries.

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?

Three short sentences with no waste, leading with what is listed before the return shape and the caveat. Each sentence contributes something the agent would otherwise have to guess.

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?

With an output schema present and zero parameters, the description need not explain return structure or arguments, and it does not overreach. The one gap is that it never connects this discovery call to get_company_leaderboard, which would complete the workflow for an agent.

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?

The tool takes zero parameters, which sets the baseline at 4; there is nothing for the description to disambiguate. The stated output fields are extra context rather than a substitute for parameter documentation.

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 and resource ('List supported AI company leaderboards') and names the dimensions available (revenue, country/region, category). The list-vs-get split against the sibling get_company_leaderboard is implied by 'supported' but the sibling is never named, so differentiation is inferential rather than explicit.

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?

Usage is only implied: an agent can infer this is the discovery call that yields IDs for get_company_leaderboard, but that routing is never stated. There are no explicit when/when-not conditions or named alternatives.

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