Skip to main content
Glama

kling_generate_video

Generate AI video from text prompts by describing the scene, motion, style, and mood. Choose model, aspect ratio, duration, and mode. No reference images needed. Output includes task ID and 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-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.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'.
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
Behavior3/5

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

No annotations exist, so description should carry the burden. It mentions return types (Task ID and video info) but does not disclose asynchronous behavior, potential delays, or any side effects. The description lacks details on polling or webhook usage beyond mentioning callback_url.

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

Conciseness4/5

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

The description is well-structured with clear sections (overview, when to use, alternative, returns). It is front-loaded and concise, though the returns section could be integrated into text rather than listing.

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?

Given 11 parameters and output schema, the description covers main use case and alternatives but lacks crucial async behavior information (task polling). It is moderately complete but missing key operational context.

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 baseline is 3. The description adds value for prompt (examples and guidance) and notes about alternatives, but most parameter details are already in 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 clearly states 'Generate AI video from a text prompt using Kling.' It uses a specific verb ('generate') and resource ('AI video') and distinguishes from sibling tools like kling_generate_video_from_image by noting use cases for this tool.

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?

Explicitly lists when to use this tool (text description, no reference images, quick generation) and when to use an alternative (kling_generate_video_from_image for reference images). Provides clear context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AceDataCloud/KlingMCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server