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kling_generate_video_from_image

Animate a video from a start image to an end image, generating smooth motion between them using a text prompt.

Instructions

Generate AI video using reference images as start and/or end frames.

This allows you to control the video by specifying what the first frame
and/or last frame should look like. Kling will generate smooth motion between them.

Use this when:
- You have a specific image you want to animate
- You want to create a video transition between two images
- You need precise control over the video's visual content

At least one of start_image_url or end_image_url must be provided.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesDescription of the video motion and content. Describe what should happen in the video, how objects should move, what transitions to include.
start_image_urlNoURL of the image to use as the first frame of the video. The video will animate from this image.
end_image_urlNoURL of the image to use as the last frame of the video. The video will animate towards this image.
modelNoKling model to use. Default: 'kling-v2-master'.kling-v2-master
modeNoGeneration mode. 'std' (standard, default), 'pro' (higher quality), or '4k' (native 4K, only for kling-v3 and kling-v3-omni).std
aspect_ratioNoVideo aspect ratio. Usually should match your input image ratio.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).
negative_promptNoThings to avoid in the video.
cfg_scaleNoClassifier-free guidance scale. Higher values follow the prompt more strictly.
camera_controlNoCamera control as JSON string.
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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must fully disclose behavior. It explains the image-controlled generation and return of task info, but does not mention potential long-running nature, rate limits, or that the tool is a write operation (generates and consumes credits). The description is adequate but not comprehensive.

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 well-structured with a brief opening sentence, bullet-pointed use cases, and a clear requirement. Every sentence adds value, and the entire description is concise without unnecessary details.

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 15 parameters and the presence of an output schema, the description provides sufficient context on what the tool does and when to use it. It covers the core functionality and return type. Additional details about parameters like prompt or camera control are not necessary here but would not hurt.

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 parameter descriptions are already provided. The tool description restates key parameters (start/end image URL) and their role, but does not add significant new meaning beyond the schema. 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 using reference images as start and/or end frames.' It specifies the verb (generate), resource (video from images), and distinguishes from siblings like kling_generate_video (text-to-video) and kling_extend_video.

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

Usage Guidelines4/5

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

The description provides explicit use cases ('Use this when: ...') and a requirement ('At least one of start_image_url or end_image_url must be provided'). It does not explicitly state when not to use, but the guidance is clear and helpful.

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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