Generate a sound effect from a text prompt. This consumes credits. Renders synchronously: the result usually returns immediately with output_urls.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| duration_seconds | No | ||
| prompt_influence | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the behavioral disclosure burden. It usefully discloses that the operation consumes credits, renders synchronously, and returns output_urls, which are non-obvious traits an agent should know before calling.
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 concise and front-loaded, stating purpose in the first sentence, followed by two short high-value behavioral notes. Every word earns its place.
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?
The description covers purpose, synchronous behavior, cost, and result shape via output_urls, but it omits how the optional duration and prompt_influence parameters affect the result and does not differentiate use cases from sibling audio-generation tools.
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 0%, so the description must compensate for missing parameter semantics. It only clarifies that generation happens from a text prompt via the prompt parameter, but offers no meaning or usage guidance for duration_seconds or prompt_influence.
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 states a specific verb and resource: 'Generate a sound effect from a text prompt.' This clearly distinguishes the tool from siblings like generate_image, generate_music, generate_speech, and generate_video.
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 about when to choose this tool over the closely related generate_music or generate_speech tools. The description gives operational context (credits, sync rendering) but no explicit when-to-use or alternative-selection guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool targets a distinct resource/action: generate_* tools are separated by media type, list_*/get_* tools cleanly separate overview from detail retrieval, and get_task vs wait_for_task are clearly one-shot status vs polling behavior. There is no real overlap or ambiguity among the 14 tools.
The tools follow a consistent verb_noun snake_case convention: generate_*, list_*, get_*, and wait_for_*. The generate_* group cleanly maps to each output modality, and the get/list distinction is applied predictably.
14 tools is well-scoped for a multimodal generation server. Each tool earns its place: generation for each media type, model listing/detail, voice enumeration, credit lookup, and task status handling. There is no obvious bloat or redundancy.
The surface covers the core workflow well: discover models/voices, create generations, retrieve outputs, and monitor credits. The main gap is the absence of an explicit task cancellation tool, but the persisted task statuses and wait_for_task workflow make this a minor gap rather than a blocking one.
