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extend_video

Extend a Kling v2.5 Turbo video using a completed source task ID. Creates a task on RunAPI and returns task ID, status, and output URLs.

Instructions

Create a Kling task on RunAPI (extend video). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoExtension quality mode. Declared type: string. Known values: "std", "pro".
waitNoPoll until the task reaches a terminal status.
modelNoRunAPI model slug for this model line.
promptNoOptional description for the continuation. Declared type: string.
timeout_msNo
callback_urlNoWebhook URL for async notifications. Declared type: string.
source_task_idYesCompleted Kling v2.5 Turbo source task ID. Declared type: string.
poll_interval_msNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed8 schema fields changedv0.3.0
    • addedInput schema / additionalProperties
      Added value: +{}
    • changedInput schema / properties / callback_url / description
      Previous value: -"Webhook URL for async notifications."New value: +"Webhook URL for async notifications. Declared type: string."
    • removedInput schema / properties / mode / anyOf
      Removed value: -[
      -  {
      -    "const": "std",
      -    "type": "string"
      -  },
      -  {
      -    "const": "pro",
      -    "type": "string"
      -  }
      -]
    • changedInput schema / properties / mode / description
      Previous value: -"Extension quality mode."New value: +"Extension quality mode. Declared type: string. Known values: \"std\", \"pro\"."
    • addedInput schema / properties / mode / type
      Added value: +"string"
    • removedInput schema / properties / model / enum
      Removed value: -[
      -  "kling-v2.5-turbo-image-to-video-pro",
      -  "kling-v2.5-turbo-text-to-video-pro"
      -]
    • changedInput schema / properties / prompt / description
      Previous value: -"Optional description for the continuation."New value: +"Optional description for the continuation. Declared type: string."
    • changedInput schema / properties / source_task_id / description
      Previous value: -"Completed Kling v2.5 Turbo source task ID."New value: +"Completed Kling v2.5 Turbo source task ID. Declared type: string."
  2. Changed6 schema fields changedv0.2.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / mode / anyOf
      Added value: +[
      +  {
      +    "const": "std",
      +    "type": "string"
      +  },
      +  {
      +    "const": "pro",
      +    "type": "string"
      +  }
      +]
    • removedInput schema / properties / mode / enum
      Removed value: -[
      -  "std",
      -  "pro"
      -]
    • removedInput schema / properties / mode / type
      Removed value: -"string"
    • addedInput schema / properties / poll_interval_ms / maximum
      Added value: +9007199254740991
    • addedInput schema / properties / timeout_ms / maximum
      Added value: +9007199254740991
  3. Addedv0.1.13

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full disclosure burden. It does mention that a task id, status, and output URLs are returned, hinting at an async job model, but says nothing about cost, authentication, rate limits, whether the source task must be Kling v2.5 Turbo specifically, or how wait/callback interact.

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?

Two short sentences, front-loaded with the core action and followed by the return contract; nothing is wasted. It is marginally terse given eight parameters, but there is no filler.

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?

For an eight-parameter async task-creation tool with no output schema, the description partially compensates by naming the return fields (task id, status, output URLs). However, with no annotations it leaves async behavior, callback_url/polling semantics, and cost unaddressed, so it is minimally adequate rather than complete.

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 75%, so most parameters are self-documenting and the baseline is 3. The description adds no meaning beyond the schema, and two parameters (timeout_ms, poll_interval_ms) have no description in either place, so it does not compensate for that gap.

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 states a concrete verb and resource ('Create a Kling task ... extend video'), so an agent can tell it produces a video extension rather than a fresh generation. It does not explicitly differentiate itself from siblings like text_to_video or image_to_video, but the action is unambiguous.

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

Usage Guidelines2/5

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

There is no statement of when to use this versus alternatives such as text_to_video or edit_video, nor any prerequisites beyond the implicit need for a completed source task implied by the required source_task_id. The agent must infer that this continues an existing generation rather than starting one.

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