List languages and models

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds no behavioral context beyond that - no indication of return format, cost, or whether the listing is static or environment-dependent, despite the openWorldHint=false implying a fixed local catalog.

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 short fragment with no wasted words, and the key information (models + per-language --accurate mapping) is front-loaded. It is terse to the point of being a label rather than a sentence, which slightly limits clarity.

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 zero-parameter lookup with no output schema, the description conveys what kind of data comes back but not its shape (e.g., list vs. language-keyed map) or how it relates to other tools. Adequate but leaves the agent guessing about the return structure.

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?

The tool takes zero parameters, so per the rubric the baseline is 4. The schema is trivially complete and the description correctly implies the output is a static mapping rather than something parameterized.

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 the resource (available models) and adds a specific mapping detail ('which model --accurate uses per language'), which helps distinguish it from siblings like transcribe. However, it is a noun fragment with no verb, so the agent must infer that the tool lists/enumerates rather than computes or modifies anything.

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 explicit statement of when to call this tool or what it replaces. The reference to the '--accurate' flag hints at a pre-transcription lookup use case, but the agent must infer that this is a discovery step before calling transcribe, and there are no exclusions or alternatives named.

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