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

Annotations already cover read-only, idempotent and open-world behavior, so the lower bar applies. The description adds real semantic context beyond that: what the response contains (ranks, ties, USD revenue, confidence, sources) and the warning that revenue, ARR, run-rate and forecasts carry distinct basis labels, which prevents an agent from conflating metrics.

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, front-loaded with purpose, then payload, then the caveat. No filler; the example IDs and basis-label warning each earn their place.

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?

An output schema exists, so return value detail is optional and the description's summary is a bonus. The only real gap is not pointing an agent at list_company_leaderboards to obtain valid IDs, which matters for a required exact-match parameter.

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 67% and the required 'leaderboard' parameter has no description there, so the description meaningfully compensates by giving concrete example IDs (ai-global, ai-usa, ai-china). The limit/offset pagination semantics are already well documented in the 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?

States a specific verb and resource ('returns a published AI company leaderboard') plus the key scoping constraint that it takes an exact ID. It doesn't explicitly name how it differs from list_company_leaderboards or get_solution_rankings, so an agent must infer the boundary.

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?

'By its exact ID' implies this is a lookup rather than a discovery tool, but the description never says to call list_company_leaderboards first to find IDs, nor when to prefer get_solution_rankings. Usage is only implied.

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