Convert text to speech with a selected voice_id (see list_voices). This consumes credits. Speech renders synchronously: the result usually returns immediately with output_urls.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| speed | No | ||
| model_id | No | ||
| voice_id | Yes | ||
| stability | No | ||
| similarity_boost | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description correctly carries the behavioral burden. It discloses two important non-obvious behaviors: credit consumption and synchronous rendering with output_urls immediately. This is valuable information beyond the parameter schema, though it stops short of describing failure modes, credit limits, or authentication requirements.
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?
Two concise sentences deliver purpose, prerequisite, cost implication, and expected timing in a compact, front-loaded way. There is 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?
The description covers the most important context: what it does, how to pick a voice, credit usage, synchronous behavior, and the output form (output_urls). However, it omits any mention of fallback behavior when rendering is not immediate, model selection, or optional tuning parameters. Still, the core invocation path is reasonably covered.
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 parameter coverage is 0%, and the description only mentions text and voice_id, the two required parameters. Four optional parameters (speed, model_id, stability, similarity) are left completely undocumented, so the agent has to infer their meanings. This does not sufficiently compensate for absent schema descriptions.
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 identifies the tool's action ('Convert text to speech'), the target resource (text and voice_id), and explicitly distinguishes it from image, music, sound effect, and video generation siblings. The reference to list_voices reinforces the speech-specific scope.
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 solid context for when to use it: it consumes credits and renders synchronously with immediate output_urls, which lets an agent avoid waiting for tasks or using wait_for_task. It also points to list_voices for selecting a voice. However, it does not explicitly state when not to use it or mention alternatives beyond that.
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.
