Create a video generation task. Ultra ViewMax C1 generations cost 0 credits within the daily fair-use pool (also via REST API); otherwise this consumes credits from the authenticated ViewMax account.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | ||
| audio | No | ||
| model | Yes | ||
| prompt | Yes | ||
| duration | No | ||
| image_urls | No | ||
| resolution | No | ||
| video_urls | No | ||
| aspect_ratio | No | ||
| source_video_duration_seconds | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure. It usefully reveals the credit-consumption behavior and the fair-use pool, which is valuable. But it remains silent on asynchronous behavior, return format, auth requirents beyond 'authenticated ViewMax account', and side effects.
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, zero fluff, with the core purpose first and the cost/billing note second. The description earns each sentence despite being concise.
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 10-parameter tool with no output schema and no annotations, the description is markedly under-specified. It does not explain return values, task lifecycle, the meaning of the parameters, or how it fits with wait_for_task and get_task. An agent cannot confidently map inputs or interpret results from the description alone.
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% by context. The tool has 10 parameters, including mode, audio, image_urls, video_urls, resolution, aspect_ratio and duration, yet the description defines none of them. It adds no semantic value beyond the bare schema.
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 the direct function: 'Create a video generation task.' The resource is clearly video generation, which distinguishes it from sibling tools like generate_image, generate_music, and generate_speech.
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 primary usage is implied — this is the video-generation tool among siblings — and the credit/fair-use note adds important context about when costs apply. However, there is no explicit guidance on when to prefer this tool over alternatives, nor any mention of related tools like wait_for_task.
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.
