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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds real value by disclosing what data is returned — aggregate rank, rating with standard error, cost-per-task availability ('where known') — which is important since there is no output schema. It does not state failure behavior for unknown model names.

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 sentence with the core concept ('One model in depth') front-loaded, followed by a compact enumeration of returned fields. Every clause carries information, though the list-like tail is dense.

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 carries the burden of describing the return payload, and it does so thoroughly. Only edge-case behavior (unknown model, missing cost data beyond 'where known') is left unaddressed.

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?

There is a single parameter with 100% schema description coverage, and the schema already gives example formats ('Claude Opus 4.5' or 'gpt-5-5'). The description adds nothing about name-vs-slug resolution or case sensitivity, 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 specific resource ('One model in depth') and enumerates the exact payload: rank, Aggregate Rating with standard error, provider, description, cost per task, and benchmark results. This clearly separates it from list-oriented siblings like search_models or get_leaderboard, though no sibling is named explicitly.

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 implied by the phrasing 'One model in depth' — an agent can infer this is the single-entity lookup versus the multi-entity siblings. However, no explicit when-to-use guidance, prerequisites, or alternative routing (e.g., compare_models for side-by-side) is given.

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