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AceDataCloud

KlingMCP

by AceDataCloud
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Python
Hybrid

kling_generate_video

Generate AI videos from text descriptions without reference images. Describe scenes, motion, and style to create video content and receive task IDs with generated video URLs.

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-video-o1'.kling-v2-master
modeNoGeneration mode. 'std' (standard, default) for faster generation, 'pro' for higher quality.std
aspect_ratioNoVideo aspect ratio. Options: '16:9' (landscape, default), '9:16' (portrait), '1:1' (square).16:9
durationNoVideo duration in seconds. Options: 5 (default) or 10.
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'.
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

Tool Definition Quality

A4.2/5.0
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses return structure ('Task ID and generated video information including URLs and state') implying async behavior, and claims 'high-quality' output. However, it omits operational details like credit costs, typical generation time, content moderation policies, or explicit async polling requirements despite returning a Task ID.

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?

Excellent structure with one-line purpose, elaboration, bulleted usage conditions, explicit alternative reference, and returns block. Every sentence provides distinct value; no repetition of schema details or annotations.

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 10 parameters with 100% schema coverage and existence of output schema, the description appropriately focuses on sibling differentiation and return value summary rather than parameter minutiae. Completeness would be perfect with explicit mention of the async polling pattern (implied by Task ID and existence of kling_get_task sibling but not stated).

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%, establishing a baseline of 3. The description mentions 'text prompt' reinforcing the required parameter, but adds no additional syntax guidance, examples, or constraint explanations beyond what the schema already provides.

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 opens with a specific verb+resource+mechanism ('Generate AI video from a text prompt using Kling') and explicitly distinguishes from the sibling 'kling_generate_video_from_image' by stating this is for when 'you don't have 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 this when:' bullet points covering the primary use case (text description), prerequisite absence (no reference images), and quality attribute (quick generation). Explicitly names the sibling alternative 'kling_generate_video_from_image' for image-based workflows.

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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