LLM leaderboard

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

Annotations already establish readOnlyHint, idempotentHint and openWorldHint, so the safety profile is covered. The description adds genuinely new context: the 0-100 score scale and the per-row contents (score, trend, status, stability, last updated), which matters because there is no output schema.

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?

Two sentences, front-loaded with the identity and scale of the resource before the use cases and row contents. Efficient, though the parenthetical vendor list is borderline redundant with the schema's vendor parameter.

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 no output schema, the description usefully compensates by enumerating the fields each row contains. Purpose, usage and output shape are all covered; only cross-tool routing details and pagination-like behavior are absent, which is minor for a read-only 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?

Schema description coverage is 100%, so the schema already documents limit, period, vendor and sort_by thoroughly, including defaults and the paid-key requirement for 'latest'. The description echoes the ranking dimensions (coding, reasoning, tool use) but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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 states a concrete resource (a ranking of tracked LLM APIs) and a concrete output (benchmark score 0-100), and it distinguishes itself from siblings by noting it supplies the model ids the other tools accept. An agent can tell this apart from get_model or get_recommendations without opening a schema.

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?

It gives explicit usage contexts: finding the best model overall or for coding/reasoning/tool use, comparing models, and obtaining model ids for other tools. It does not name alternatives or state when not to use it, but the routing intent is clear.

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