Skip to main content
Glama

kling_generate_video

Generate AI video from a text description. Specify 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
Behavior4/5

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

Despite no annotations, the description discloses the generative nature, the text-to-video process, and the return format (Task ID, URLs, state). It implies asynchronous behavior through the callback_url parameter, though not explicitly stated. The absence of destructive actions is clear. Some details like rate limits or auth are missing, but the core behavior is transparent.

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 and well-structured, starting with a clear purpose, followed by bullet points for usage guidance, an alternative tool mention, and a return format summary. Every sentence serves a purpose, and there is no redundancy.

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 present), the description covers the main aspects: purpose, use cases, alternatives, and return values. It acknowledges asynchronous behavior via the callback_url parameter. While it could include more details about model-specific behaviors or constraints, it is sufficiently complete for typical use.

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?

With 100% schema coverage, the description does not need to add much parameter detail. It provides usage context and directs users to another tool for reference images, but it does not elaborate on parameter semantics beyond what the schema already offers. The baseline of 3 is appropriate since the schema handles parameter documentation.

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 the tool's purpose: 'Generate AI video from a text prompt using Kling.' It uses a specific verb (generate) and resource (AI video), and differentiates from the sibling tool kling_generate_video_from_image by explicitly noting that the latter is for reference images. The 'simplest way' qualifier further clarifies its role.

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 when-to-use scenarios (text prompt, no reference images, quick generation) and a clear when-not-to-use (for reference images, use kling_generate_video_from_image). It does not cover all sibling tools (e.g., kling_generate_motion), but the primary alternative is addressed, making the guidance useful.

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