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

The description reveals important behavior beyond the sparse annotations: per-character pricing with a minimum charge, asynchronous execution, and the return of a generation_id that must be polled. It does not cover failure states or timeout behavior, but the main behavioral surprises are disclosed.

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 compact sentences carry substantial value: purpose, cost, and lifecycle contract. It is front-loaded with the core operation and contains no filler or repetition.

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 three-parameter tool with a complete schema, the description covers everything needed to invoke it correctly: input text, optional enums, cost, asynchronous behavior, and the explicit polling handoff to get_generation. No output schema exists, but the description tells the agent where to fetch the result.

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 coverage is already 100%, and the description adds no per-parameter semantics beyond the schema. The pricing note gives useful context about text cost, but emotion and voice_id behavior remain entirely defined by the schema and enum values.

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 opens with a precise operation: text becomes natural speech, which is instantly distinguishable from the many sibling tools such as ai_speech_to_text, ai_voice_changer, ai_audio_translator, and ai_music_generator. It combines a specific verb, resource, and transformation.

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 clear usage context: it is billed, runs asynchronously, and requires polling via get_generation until status is 'completed'. It does not explicitly name alternatives or state when not to use the tool, so it falls just short of full alternative guidance.

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