Start a text-to-video generation (async). Returns a task_id; poll get_video_status until done.
Args:
prompt: description of the video.
duration: seconds (e.g. 5).
resolution: 480p / 720p / 1080p.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| duration | No | ||
| resolution | No | 720p |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It clearly discloses that the operation is asynchronous, returns a task_id, and requires polling. This is key behavioral context, though it doesn't mention error handling, rate limits, or 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?
The description is extremely concise: a single clear lead sentence and a compact parameter list. Every line adds value, and the async flow is front-loaded. No wasted words or redundant details.
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 output schema exists, the description doesn't need to detail return values. It covers the essential workflow: initiate task, receive task_id, poll status. The parameter semantics are also covered. It lacks notes on potential errors or auth, but for a simple asynchronous generation tool, it is reasonably complete.
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 add meaning. It enriches the schema by providing examples: duration as seconds with 'e.g. 5', and resolution as '480p / 720p / 1080p', which clarifies allowed values beyond the schema defaults.
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 states 'Start a text-to-video generation (async)' with a specific verb and resource. It distinguishes itself from siblings like generate_image and get_video_status by emphasizing the asynchronous task creation and return of a task_id.
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 says 'poll get_video_status until done', providing direct guidance on the follow-up tool to use. It implies the tool is for initiating video generation, but doesn't explicitly mention when to avoid using it or compare it to generate_image beyond the async nature.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool maps to a distinct modality or lifecycle step: chat, image generation, video generation, video status polling, model listing, and speech synthesis. The async video pair (generate_video/get_video_status) is clearly separated, and no two tools plausibly do the same thing.
Most tools follow a clear verb_noun pattern: generate_image, generate_video, get_video_status, list_models. The deviations are minor: 'chat' is a bare verb and 'text_to_speech' uses a noun_phrase style instead of generate_speech or synthesize_speech, but the overall naming is still predictable.
Six tools is well-scoped for a multimodal AI gateway covering chat, image, video, speech, and model discovery. Each tool earns its place and the count is comfortably within the ideal range.
The core workflows are covered: chat has a direct call, video generation has an async create-and-poll lifecycle, image and speech have generation entry points, and list_models aids discovery. Minor gaps exist—no image-to-video, speech status polling, or model selection for image/video/speech—but agents can accomplish the primary stated purposes.