list_video_models
List available video models and their capabilities. Video generation consumes credits; use get_video_model for detailed pricing.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
List available video models and their capabilities. Video generation consumes credits; use get_video_model for detailed pricing.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden. The warning that 'Video generation consumes credits' implicitly indicates this listing operation does not generate videos, but it does not explicitly state that listing is read-only or free of credit consumption. The absence of an explicit safety profile leaves some ambiguity.
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 a single sentence that front-loads the core purpose and adds the most relevant caveat about credits and pricing. Every phrase earns its place, with no redundant or filler content.
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 parameterless tool with no output schema, the description sufficiently conveys that the result is a list of video models with their capabilities, and it directs the agent to get_video_model for pricing. It could have stated that the operation is non-consuming explicitly, but the tool is simple enough that this is a minor gap.
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 has zero parameters, and the input schema is empty, so there is nothing meaningful to document. The description adds no parameter-specific semantics, which is expected here. The baseline for zero-parameter tools is 4.
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 clear verb-resource pairing: 'List available video models and their capabilities.' It also differentiates itself from the sibling get_video_model by noting that detailed pricing is obtained there rather than in this listing.
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 explicitly routes the agent to get_video_model when detailed pricing is needed, which gives a clear usage boundary. It does not enumerate other alternatives like list_image_models or generate_video, but the domain of video models is clear enough that an agent will know when to choose it.
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.