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

Annotations already cover read-only, non-destructive, closed-world, so the bar is lower, but the description still adds real value: it discloses the return payload contents (provider labels, cost tiers) and, importantly, that failures come back as `{ ok: false, error }` rather than throwing, with instructions not to retry blindly. That error-envelope behavior is not derivable from the annotations or 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?

Purpose is front-loaded in the first sentence; the error-handling guidance follows in a compact second and third sentence. The error guidance is somewhat verbose but earns its place because the failure mode is otherwise undocumented.

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 returns and does so at a high level (models, provider labels, cost tiers) plus the error shape. It leaves out list ordering/pagination and whether models are filtered by plan quota, but nothing that would cause a mis-call.

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 both parameters (`context`, `llm_model`) are documented at length in the schema itself. The description adds nothing about parameters, 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?

States a specific verb and resource ('List available AI Visibility LLM models') plus scope ('for the organization's plan') and return content ('provider labels and cost tiers'). It is clearly distinguishable from siblings like convert-llm-model or create-llm-run, though the description never names an alternative 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?

There is no statement of when to call this versus alternatives (e.g. before convert-llm-model or estimate-llm-setup-cost) and no preconditions beyond 'the organization's plan'. Usage is only implied by the resource name; an agent must infer the call site.

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