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

Annotations already declare readOnlyHint=false, idempotentHint=false, openWorldHint=false, and destructiveHint=false, which tells the agent this is a non-idempotent local write-ish operation. The description adds nothing beyond restating the tool name — no mention of cost, actor availability requirements, persistence of generated audio, or how repeated calls differ.

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 short, but this is under-specification rather than conciseness — a single six-character noun phrase that omits all operational detail the agent needs.

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, no sibling differentiation, six undocumented parameters, and a one-line description, the definition is far too thin for a 6-parameter synthesis tool. Nothing tells the agent how to form a valid call or what comes back.

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?

Six parameters exist (script, actor_id, speed, stability, similarity, auto_emotion) with 0% schema description coverage, so the description carries the full burden. It documents none of them, leaving the agent to guess at value ranges, units, or which are required.

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 phrase "系统演员语音生成" identifies the resource (system actor voice) and an implied action (generate), so an agent can roughly infer text-to-speech for a virtual actor. However, it is a terse noun phrase with no verb framing and gives no basis for distinguishing it from siblings like ai_actor_perform, ai_actor_list, or video_lip_sync.

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 guidance on when to use this tool versus alternatives such as ai_actor_perform or the video generation tools. No prerequisites, no exclusions, no context about actor selection is offered.

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