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kling_generate_video

Generate high-quality videos from text prompts with Kling AI. Supports standard and professional modes, and returns a task ID for status polling.

Instructions

Generate a high-quality video from text using Kling AI (Kuaishou). Supports standard and professional modes. Returns a task_id to poll for completion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYesKling AI API key
promptYesText description of the video to generate
modelNoModel name: kling-v1 or kling-v1-5 (default: kling-v1)
modeNoGeneration mode: std (standard) or pro (professional, slower). Default: std
durationNoVideo duration: 5 or 10 (seconds). Default: 5
image_urlNoOptional reference image URL for image-to-video
negative_promptNoWhat to avoid in the video
aspect_ratioNoe.g. 16:9, 9:16, 1:1. Default: 16:9
cfg_scaleNoGuidance scale 0-1 (default: 0.5)
Behavior2/5

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

With no annotations, the description should fully disclose behavior. It mentions modes and async task_id return, but omits that the tool requires an API key (despite api_key being in schema), rate limits, or the fact that generation is asynchronous. The authentication requirement is not stated in the description.

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?

Two sentences, front-loaded with the primary action, no redundant information. Every word earns its place.

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

Completeness2/5

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

Despite 9 parameters and no output schema, the description is sparse. It does not explain the return format of the task_id, the asynchronous nature in detail, or that authentication is required. Many gaps remain for a complex generation 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 baseline is 3. The description adds minimal extra meaning beyond the schema (e.g., 'supports standard and professional modes' maps to mode parameter). No additional semantic value for other parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a high-quality video from text using Kling AI, and mentions standard/professional modes. It differentiates from sibling kling_get_task by noting it returns a task_id for polling. However, it does not distinguish from other video generation tools like higgsfield, pika, or runway.

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

Usage Guidelines3/5

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

The description implies a workflow of generating then polling via kling_get_task, and mentions modes. However, it does not explicitly state when to use this tool over alternatives, nor does it provide when-not-to-use guidance.

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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