List the speech voices installed on this machine

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

No annotations are present, so the description carries the behavioral disclosure burden. It clearly indicates this is a read-only lookup ('Shows'), specifies the fields returned, and adds a useful caveat about per-machine availability. It could go slightly further by stating there are no side effects, but it is already transparent for a list tool.

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 carry all necessary information, with the core purpose front-loaded and the practical usage note following immediately. There is no filler or redundant phrasing.

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 simple zero-parameter list tool with no output schema, the description is complete: it states what is listed, what fields are included, and how the result should be used relative to add_speech. An agent has everything needed to call it 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?

The input schema has zero parameters and 100% schema description coverage, so there is nothing for the description to explain. Per the baseline for zero-parameter tools, no parameter semantics are needed.

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 uses a specific verb ('Shows') and names the exact resource: every offline text-to-speech voice installed on this machine. It also lists the returned attributes (gender, locale, engine), clearly distinguishing this discovery tool from the sibling add_speech.

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 gives explicit guidance to check this tool before using add_speech for a particular voice, because availability differs per machine. It names the relevant sibling and establishes the when-to-use context, though it does not formally list exclusion cases.

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