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

With no annotations, the description carries the behavioral disclosure burden. It explicitly states the default one-row-per-model behavior, the include_variants option to split by reasoning-effort settings, and paging via limit/offset. It also adds methodological context (Elo scale, IRT model) that sets expectations, though it stops short of declaring read-only status or data freshness.

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?

The description is two dense sentences that front-load the core purpose ('Top of the cross-benchmark aggregate ranking') and include only essential details: methodology, default row grouping, and pagination. Every clause earns its place with no filler.

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?

The description covers purpose, methodology, default behavior, and paging, which is sufficient for an agent to select and invoke the tool correctly. It lacks explicit return field names and error behavior, but given the simple schema and absence of an output schema, it is nearly complete for a leaderboard listing tool.

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 schema covers all three parameters with 100% coverage, so the baseline is 3. The description adds value by linking include_variants to the default fusion behavior and explaining that paging uses limit/offset, complementing the schema without redundancy.

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 clearly defines the tool's role as returning the top of a cross-benchmark aggregate ranking, with a specific methodology (IRT model over ~5,000 benchmarks). This distinguishes it from sibling tools like get_benchmark (single benchmark) and get_model (single model) by focusing on the aggregate perspective.

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 description implies usage by describing the aggregate ranking and default row-per-model fusion, and mentions paging, but it does not explicitly contrast with sibling tools or state when not to use this tool. For example, it does not say 'for a single benchmark, use get_benchmark instead.'

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