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edit_video

Submit a video editing task to Wan AI models via RunAPI. It processes the source video and returns a task id, status, and output URLs for the edited result.

Instructions

Create a Wan task on RunAPI (edit video). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
waitNoPoll until the task reaches a terminal status.
audioNo
modelNoRunAPI model slug for this model line.
promptNo
watermarkNo
timeout_msNo
multi_shotsNo
aspect_ratioNo
callback_urlNo
audio_settingNo
negative_promptNo
duration_secondsNo
poll_interval_msNo
source_video_urlNo
output_resolutionNo
source_video_urlsNo
reference_image_urlNo
enable_safety_checkerNo
enable_prompt_expansionNo
Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits. It mentions task creation and return values, but omits crucial behaviors like asynchronous task processing (requiring polling via get_task), reliance on specific input parameters (e.g., source_video_url), potential side effects, or rate limits. The phrase 'Create a Wan task' hints at asynchrony but does not clarify the wait/polling mechanism.

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 a concise single sentence that front-loads the purpose and key outputs. It avoids unnecessary verbosity. While it is sparse, the brevity is appropriate for this dimension, and the structure uses a clear cause-effect format: what it does and what it returns.

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

Completeness1/5

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

Given the tool's complexity (20 parameters, no annotations, no output schema), the description is far from complete. It does not explain required inputs, the asynchronous workflow, output format details, or how it interacts with sibling tools like get_task. An agent cannot correctly invoke this tool based solely on the description and schema.

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

Parameters1/5

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

The schema description coverage is only 10% (2 of 20 parameters have descriptions). The tool description adds no parameter explanations whatsoever, leaving critical inputs such as source_video_url, prompt, duration_seconds, and output_resolution undefined. This is inadequate for a 20-parameter tool; the description must compensate for schema gaps but does not.

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

Purpose5/5

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

The description clearly states the tool's function: 'Create a Wan task on RunAPI (edit video).' It specifies the verb ('Create'), resource ('Wan task on RunAPI'), and scope ('edit video'), differentiating it from sibling tools like text_to_video and image_to_video. The return values (task id, status, output URLs) are also explicitly mentioned.

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

Usage Guidelines3/5

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

The description implies the tool is for editing videos via the parenthetical '(edit video)', but it does not explicitly state when to use it versus alternatives or provide exclusions. There is no mention of prerequisites, such as needing a source video, or when a different tool (e.g., text_to_video) would be more appropriate.

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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