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kling_generate_video

Generate AI video from a text description. Describe the scene, motion, style, and mood to create high-quality video without reference images.

Instructions

Generate AI video from a text prompt using Kling.

This is the simplest way to create video - just describe what you want and Kling
will generate a high-quality AI video.

Use this when:
- You want to create a video from a text description
- You don't have reference images
- You want quick video generation

For using reference images (start/end frames), use kling_generate_video_from_image instead.

Returns:
    Task ID and generated video information including URLs and state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video to generate. Be descriptive about the scene, motion, style, and mood. Examples: 'A cat walking through a garden with butterflies', 'Astronauts shuttle from space to volcano', 'Ocean waves crashing on a beach at sunset'
modelNoKling model to use. Options: 'kling-v1', 'kling-v1-6', 'kling-v2-master' (default), 'kling-v2-1-master', 'kling-v2-5-turbo', 'kling-v2-6', 'kling-v3', 'kling-v3-omni', 'kling-video-o1'.kling-v2-master
modeNoGeneration mode. 'std' (standard, default) for faster generation, 'pro' for higher quality, '4k' for native 4K (only supported by kling-v3 and kling-v3-omni, not compatible with motion control).std
aspect_ratioNoVideo aspect ratio. Options: '16:9' (landscape, default), '9:16' (portrait), '1:1' (square).16:9
durationNoVideo duration in seconds. For kling-v3/kling-v3-omni: 3-15 (integer). Other models: 5 or 10.
generate_audioNoWhether to generate audio synchronously. Supported by kling-v3, kling-v3-omni, and kling-v2-6 (pro mode only). Default is false.
negative_promptNoThings to avoid in the video. Example: 'blurry, low quality, distorted faces'
cfg_scaleNoClassifier-free guidance scale. Higher values follow the prompt more strictly. Typical range: 0.0-1.0.
camera_controlNoCamera control as JSON string. Example: '{"type": "simple", "config": {"horizontal": 5, "vertical": 0, "pan": 0, "tilt": 0, "roll": 0, "zoom": 0}}'. Types: 'simple', 'down_back', 'forward_up', 'left_turn_forward', 'right_turn_forward'.
element_listNoList of reference subjects from the subject library. Each item should contain an 'element_id'. If a reference video is present, reference subjects + reference images must be ≤ 4; otherwise ≤ 7.
video_listNoList of reference videos. Each item should contain a 'video_url' (MP4/MOV, 3-10s, 720-2160px, 24-60fps, ≤200MB, max 1 video) and optionally 'refer_type' ('feature' or 'base', default 'base') and 'keep_original_sound' ('yes' or 'no').
timeoutNoTimeout in seconds for the API to return data. Default is 300.
callback_urlNoWebhook callback URL for asynchronous notifications. When provided, the API will call this URL when the video is generated.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the tool generates 'high-quality AI video' but does not disclose important traits like asynchronous operation, cost, rate limits, or what happens with unsafe prompts. The return type is mentioned but not complete behavioral context.

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?

The description is concise (two short paragraphs plus return line), well-structured, and front-loaded with the core purpose. Every sentence adds value.

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

Completeness4/5

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

Given the tool's complexity (13 parameters, output schema exists), the description covers the primary use case and sibling differentiation. Could mention asynchronous flow more explicitly, but overall is sufficient for an agent to decide to use this tool.

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 input schema already documents all parameters thoroughly. The description adds minimal extra meaning beyond stating the simplest use case. Baseline 3 is appropriate.

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 'Generate AI video from a text prompt using Kling.' It specifies the resource (video) and action (generate from text), and distinguishes from the sibling tool kling_generate_video_from_image which uses reference images.

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

Usage Guidelines5/5

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

Provides explicit use cases: when you want to create video from text, have no reference images, and want quick generation. Also gives alternative: use kling_generate_video_from_image for reference images.

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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