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runway_generate_video

Generate a video from a text prompt or image. Choose model, duration, and aspect ratio, then poll the returned task ID for completion.

Instructions

Generate a video from text or an image using Runway ML. Supports text-to-video and image-to-video. Returns a task_id to poll for completion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYesRunway API key
promptNoText description of the video to generate
image_urlNoURL of an image to animate (image-to-video mode)
modelNoModel name: gen3a_turbo (fast) or gen3a (quality). Default: gen3a_turbo
durationNoVideo duration in seconds (default: 5)
ratioNoAspect ratio e.g. 1280:768 or 768:1280 (default: 1280:768)
seedNoRandom seed for reproducibility
Behavior3/5

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

No annotations provided, so description carries full burden. Mentions async behavior (returns task_id to poll), but lacks details on rate limits, cost, or potential destruction.

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

Conciseness5/5

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

Two sentences, no fluff, front-loaded with action verb and resource. Every sentence adds value.

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

Completeness3/5

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

For a complex tool with 7 parameters and no output schema, description is functional but lacks details on output structure, model trade-offs, and polling mechanics.

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 coverage is 100%, so baseline 3. Description adds context about modes (text-to-video vs image-to-video) but does not add meaning beyond schema descriptions.

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?

Description clearly states it generates video from text or image using Runway ML, and mentions two modes. Platform name helps differentiate from other video tools, but no explicit sibling differentiation.

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?

States supports text-to-video and image-to-video, implying parameter usage. No explicit when-not-to-use, alternatives, or prerequisites beyond the schema.

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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