Model recommendations

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is fully covered. The description adds only that each pick carries its reason and evidence, which hints at response content but reveals nothing about freshness, caching, auth needs, or rate limits beyond what the schema states for 'latest'.

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 list of pick categories, and the parenthetical glosses (e.g. 'most points per measured dollar among near-top models') earn their space by disambiguating the picks. The trailing question is slightly awkward but functional.

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 what comes back, and it does so well by listing the pick categories plus the attached reason and evidence. The only real gap is the absence of any pointer to or contrast with sibling tools for an agent choosing between this and get_leaderboard.

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% and the single 'period' enum is documented in detail there, including the free-vs-paid key defaults. The description says nothing about the period parameter, so it adds no meaning beyond the schema; 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 enumerates exactly what the tool returns: coding pick, steadiest score, fastest, best value, overpriced models, and models to avoid, each with reason and evidence. That is far more specific than its siblings' surface (e.g. get_leaderboard), though it never states the verb explicitly, relying on 'current picks' and the trailing usage question to imply retrieval.

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?

'Use it for "which model should I use?"' gives a clear selection context that separates it from raw-data siblings like get_leaderboard or get_model. It offers no explicit exclusions or named alternatives, so an agent must infer that this is the curated layer rather than the underlying data.

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