tts_unload

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

With no annotations, the description carries the full burden and does reasonably well: it discloses the side effect (model unloaded, VRAM freed) and idempotency ('safe to call when nothing is loaded'). It stops short of saying whether in-flight speech is interrupted or whether a reload is required afterward.

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?

Three short sentences, front-loaded with the action, then the trigger conditions, then the safety reassurance. Every sentence earns its place with no filler.

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?

An output schema exists, so return-value explanation is unnecessary, and a zero-parameter unload tool needs little else. The only meaningful gap is the absence of explicit routing versus the sibling stop_speech.

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 baseline there is nothing for the description to document. No parameter-level gaps exist.

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: release the loaded Chatterbox model and return its VRAM to the GPU. An agent can tell this is an unload/teardown operation. It does not, however, explicitly differentiate itself from the sibling stop_speech, which arguably also halts model activity, so it falls short of the 5 bar.

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?

Gives concrete triggering scenarios: another process needs the card, or dropping a 500M model before a lighter turbo load. It also preempts the caller's main worry with 'Safe to call when nothing is loaded.' No explicit when-not-to-use or naming of stop_speech as the lighter-weight alternative, so not a 5.

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