List the professions with an AI model ranking

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

The annotations already cover the safety profile (readOnlyHint true, idempotentHint true, destructiveHint false), and the description only adds that each entry is tagged direct or indirect and shows the top-ranked model today, plus locale fallback in the schema. With an output schema present, return-shape detail is not required, so a 3 is appropriate – useful context but not rich behavioral disclosure beyond the annotations.

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 core purpose and ending with the actionable next step; the parenthetical example list earns its place by clarifying what a 'profession' means here. Minor length cost from the example enumeration.

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?

For a small read-only list tool with three optional, fully documented parameters, an output schema, and clear annotation coverage, the description supplies everything an agent needs to call it correctly and route onward.

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 two enum parameters (locale, evaluation) plus group are already documented in the schema, including the English fallback note. The description adds no parameter-level guidance of its own, so 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 names a specific verb and resource ('List the professions') and adds distinguishing scope: professions that carry a frontier-AI-model ranking built from independent benchmarks, with example job families and the direct/indirect marking. That is enough detail to separate it from generic list_* siblings and from recommend_model_for_profession without opening either 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 states the explicit purpose ('Use this to find the profession id') and names the next tool to call with the reason ('then call recommend_model_for_profession'), so the when-to-use is unambiguous and the sequencing is spelled out.

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