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

With no annotations, the description carries the full burden and does so: bearer API key requirement, cost of 2 credits with per-credit pricing, the upstream POST endpoint it proxies, and a 16 KB response cap. It also discloses output behavior (caller order preserved, unknown values stay null), which is exactly the kind of behavioral context annotations would otherwise supply.

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?

Purpose is front-loaded and every sentence carries operational facts (auth, price, endpoint, size cap). Slight waste in 'It doesn't choose a model and does not rank or choose a winner', where 'choose' is repeated twice, but the overall density is appropriate for the information delivered.

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?

There is no output schema, so the description must describe returns — and it does, listing the returned data shape (snapshot facts, offerings, evidence, sources), ordering guarantees, null handling and the 16 KB truncation limit. Combined with cost and auth disclosure, an agent has everything needed to call and interpret this correctly.

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 `models` parameter is fully documented with pattern, min/max items and an example id. The description's '2 to 4 named models' and 'in caller order' restate the schema rather than extend it, 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?

Opens with a specific verb and resource ('Which facts, offerings and evidence do 2 to 4 named models have?') and declares the boundary that separates it from the ranking/decision siblings ('does not rank or choose a winner'). An agent can distinguish it from `rank`, `decide` and `model_info` without opening any 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?

The negative case is explicit — it is not for choosing or ranking a winner — which steers agents away from misuse. However it never names the sibling tools (rank, decide) that should be used instead, so the routing is implied rather than given. The scoping to 2–4 named models is clear context for when this applies.

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