list_music_models
List available music models and their controls (duration, instrumental) and credit cost. Music generation consumes credits.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
List available music models and their controls (duration, instrumental) and credit cost. Music generation consumes credits.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the behavioral responsibility. It makes clear this is a read-only listing and adds a meaningful business context warning — music generation consumes credits. It does not go into further detail, but for a zero-parameter list tool this is adequate.
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 sentences, no fluff, front-loaded with the primary purpose and followed by a useful credit warning. Everything present 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?
Given the tool has no parameters, no annotation, and no output schema, the description adequately covers what you get: available models, controls, and costs. A more explicit mention of the output format or a note to use generate_music for actually creating tracks could have made it fully complete, but none of these are blocking for an agent selecting this simple read-only tool.
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 tool accepts zero parameters and the input schema is empty, so there are no parameter semantics to explain. With 0 params the baseline is 4, and the description's mention of controls and credit cost helps clarify what is included in the output rather than what arguments to pass.
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 specific verb and resource: 'List available music models.' It specifies exactly what the tool provides (controls: duration, instrumental, and credit cost) and clearly separates it from the generation-oriented siblings like generate_music and from other list tools like list_image_models and list_video_models.
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 implies the use case: inspect available music models and costs before generation, especially since 'Music generation consumes credits.' It does not explicitly name alternatives or say when not to use this tool, so the transfer to sibling tools is left to inference.
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.