generate_voice

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal some behavioral traits: it mentions credit consumption (via remainingCredits in returns), default values for parameters, and that the tool generates downloadable audio files. However, it lacks critical information such as rate limits, authentication requirements, whether the generated files are stored permanently, error conditions, or performance characteristics. The description adds value but leaves significant gaps.

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 well-structured with clear sections for Args and Returns, making it easy to parse. Each parameter explanation is concise yet informative. The opening statement is direct. While efficient, the Japanese text might require translation for some agents, and the Returns section could be slightly more detailed about response structure, but overall it's appropriately sized with minimal waste.

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?

Given the tool's complexity (6 parameters, voice generation with credit consumption) and the absence of both annotations and output schema, the description does a decent job but has notable gaps. It explains parameters thoroughly and outlines the return structure, which helps compensate for the missing output schema. However, it lacks information about error handling, authentication, rate limits, and the relationship with sibling tools, making it incomplete for safe and effective use by an AI agent.

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 description provides excellent parameter semantics beyond the input schema, which has 0% description coverage. For all 6 parameters, it explains their purpose, constraints (e.g., script max 3000 characters, speed range 0.4-3.0), default values, and special meanings (e.g., -1 for emotional_level means use voice actor's default). This fully compensates for the schema's lack of descriptions and adds meaningful context that the schema alone doesn't provide.

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 clearly states the tool's purpose as '音声を生成してその結果を返す' (generate voice and return the result), which is a specific verb+resource combination. It distinguishes itself from sibling tools like get_credit_balance and get_voice_actors by focusing on voice generation rather than querying resources. However, it doesn't explicitly contrast with potential alternative generation tools beyond the siblings listed.

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 provides no guidance on when to use this tool versus alternatives. While it mentions that actor_id defaults to the first available actor if omitted, it doesn't explain when to specify an actor_id versus using the default, or how this tool relates to the sibling tools (e.g., whether get_voice_actors should be called first to select an actor). There's no mention of prerequisites, constraints, or typical use cases.

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