list_languages

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that it lists supported codes and names, but does not disclose any behavioral traits such as authentication needs, rate limits, or idempotency. For a simple read-only list, the description is adequate but lacks depth.

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?

The description is a single concise sentence that is front-loaded and contains no wasted words. Every word is necessary.

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?

Given zero parameters and the existence of an output schema, the description is complete enough for an agent to understand and use the tool correctly. The sibling tool name provides context for when to call this tool.

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?

There are 0 parameters, and schema coverage is 100%. The description adds meaning by specifying that the output includes language codes and names, which is not evident from the empty schema. This provides useful context beyond the schema.

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 clearly states the tool lists language codes and names for text_to_speech. It uses a specific verb (list) and resource, and distinguishes from the sibling tool text_to_speech which performs speech synthesis.

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 description implies the tool is used to get supported languages before using text_to_speech, but it does not explicitly state when to use it or provide alternatives or exclusions. The context signals include a sibling tool name, which hints at the connection, but no explicit guidance.

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