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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and non-destructive, so the safety profile is covered. The description adds what a read actually returns (speakerId, name, a sample, supported emotions) and the backing endpoint, which is meaningful value beyond the annotations.

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 dense sentence front-loads the resource and follows with the consumers and returned fields. Nothing is wasted, though the stacked parentheticals and trailing endpoint make it slightly run-on rather than crisply structured.

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, the description compensates by enumerating the returned fields (speakerId, name, sample, emotions) and linking the voices to their consuming tools, which is what an agent needs to call and use this correctly. Missing only explicit guidance on account scoping for the email parameter.

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?

Schema description coverage is 100%, so the single email parameter is fully documented in the schema and the description need not repeat it. The description adds nothing about the parameter, so the baseline 3 applies.

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+resource (Kling's text-to-speech voices) and immediately distinguishes it from the generic kling_list_voices sibling by naming the exact consumer tools (kling_text_to_speech, kling_avatar_video, kling_create_avatar). An agent can tell what it returns (speakerId, name, sample, supported emotions) without opening the schema.

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?

By naming the three tools that consume these voices it implies the when-to-use condition (call before invoking TTS/avatar tools), giving clear context. It stops short of explicitly routing away from the similar kling_list_voices / kling_list_element_voices siblings, so no exclusions are stated.

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