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text_to_video

Create a text-to-video generation task on RunAPI. Submit a prompt and receive a task ID, status, and output URLs for the generated video.

Instructions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
waitNoPoll until the task reaches a terminal status.
modelNoRunAPI model slug for this model line.
ratioNo
promptNo
watermarkNo
timeout_msNo
accelerationNo
aspect_ratioNo
callback_urlNo
negative_promptNo
duration_secondsNo
poll_interval_msNo
output_resolutionNo
reference_audio_urlNo
background_audio_urlNo
reference_image_urlsNo
reference_video_urlsNo
enable_safety_checkerNo
first_frame_image_urlNo
enable_prompt_expansionNo
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 return value (task id, status, output URLs) but does not disclose that this is an asynchronous operation, potential side effects like costs or rate limits, or prerequisites such as authentication. The absence of these details for a task-creating tool is a significant gap.

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

Conciseness3/5

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

The description is a single, front-loaded sentence with no waste, but it is under-specified for a tool with 21 parameters and complex behavior. It is concise but not appropriately sized to convey necessary information, making it borderline adequate.

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?

Given the high complexity (21 parameters, no output schema, no annotations), the description is far from complete. It only covers the high-level purpose and return format, omitting usage context, parameter guidance, and behavioral details that are critical for correct invocation.

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

Parameters2/5

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

Schema description coverage is only 10%, so the description must compensate by explaining key parameters. It does not mention any parameter semantics, leaving most of the 21 parameters undocumented in both the schema and description. This fails to add meaningful value beyond the bare schema.

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 states a specific verb ('Create') and resource ('Wan task on RunAPI') with an explicit scope ('text to video'). It clearly distinguishes this from sibling tools like image_to_video and speech_to_video by naming the modality.

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 this tool is for text-to-video generation, but it does not explicitly state when to use it over alternatives or provide exclusions. The context signals show many sibling tools, so more guidance on selection would be helpful, but the purpose is clear enough that usage is implied.

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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