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create_video

Generate AI videos from text prompts using Sora 2 models. Customize resolution, duration, and model selection for video creation.

Instructions

Generate a video using Sora 2 or Sora 2 Pro model. Creates a video from a text prompt with optional parameters for resolution, duration, and model selection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesText description of the video to generate
modelNoModel to use for generation. sora-2 is faster and cheaper, sora-2-pro provides higher quality.sora-2
sizeNoVideo resolution (width x height)1280x720
secondsNoVideo duration in seconds8
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the creation action but lacks critical details: it doesn't specify whether this is a synchronous or asynchronous operation (unlike 'get_video_status' which suggests async), doesn't mention rate limits, costs, permissions needed, or what happens on failure. The description is insufficient for a mutation tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with two sentences that efficiently convey the core functionality. The first sentence states the main purpose, and the second adds parameter context without unnecessary elaboration. It could be slightly more structured by front-loading more critical behavioral information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a video generation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., video URL, job ID, error formats), doesn't address async behavior hinted at by sibling 'get_video_status', and provides minimal guidance on parameter trade-offs despite the schema doing most of the work.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by mentioning 'optional parameters for resolution, duration, and model selection' but doesn't provide additional semantic context beyond what's in the schema. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Generate a video') and the resource ('using Sora 2 or Sora 2 Pro model'), with specific details about creating from a text prompt. It distinguishes from siblings like 'create_fade_animation' by specifying the model type, but doesn't explicitly contrast with other video-related tools like 'merge_videos' or 'delete_video'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like 'create_fade_animation' or 'merge_videos'. It mentions optional parameters but doesn't explain scenarios where one might choose this over sibling tools, leaving the agent without contextual usage direction.

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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