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

Beyond the annotations (readOnlyHint=false, openWorldHint=true), the description adds valuable behavioral context: the tool is asynchronous and returns a generation_id, and it has a measurable cost of $0.10 per 1,000 characters. This goes well beyond what the annotations convey, though it doesn't discuss failure modes, inputs that get rejected, or security/consent enforcement.

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 with zero filler: purpose first, then cost, then the async flow. Every sentence earns its place and the most critical operational fact (poll get_generation) is stated explicitly.

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 5-parameter, asynchronous tool with no output schema, the description covers the critical success path (poll get_generation) and cost model. Minor gaps remain — no mention of what the completed generation returns, no clarification of consent implications, and no failure/error behavior — but an agent has enough to invoke and verify the call 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?

Schema description coverage is 100%, so the schema already documents all five parameters, including the consent requirement and the audio format for reference_audio. The baseline of 3 applies; the description's character-based pricing and async note add operational context but no per-parameter semantic detail 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?

"Clone a voice from a sample" is a specific verb+resource with a clear scope. The 'from a sample' qualifier implicitly distinguishes it from siblings like ai_voice_generator and ai_voice_changer, but the description never names those alternatives explicitly, so differentiation is left to inference.

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 no guidance on when to choose this tool over ai_voice_generator, ai_voice_changer, or ai_audio_translator. The async polling note ('call get_generation until status is completed') is a flow instruction, not usage-selection guidance; no prerequisites, exclusions, or alternative conditions are stated.

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