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AceDataCloud

KlingMCP

by AceDataCloud
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

kling_lip_sync

Replace a video's lip movements with speech from an audio file or generated text, enabling dubbing and talking-head videos.

Instructions

Synchronize lip movements in a video to match a given audio track or text.

Takes an existing video (by URL or task ID) and replaces the speaker's lip
movements so they match the provided audio. In 'text2video' mode the audio
is generated from the supplied text via TTS.

Use this when:
- You want to dub a video into a different language
- You want to replace the audio of a generated video with custom speech
- You want to create a talking-head video from text

Returns:
    Task ID and lip-sync video information.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesLip-sync mode. 'audio2video' to drive lips from an audio file or URL; 'text2video' to generate speech from text and drive the video.
textNoText to convert to speech. Required when mode='text2video'.
video_idNoTask ID of a previously generated video to use as the source. Provide either video_url or video_id.
voice_idNoVoice ID to use for text-to-speech synthesis (mode='text2video').
audio_urlNoURL of the driving audio. Required when mode='audio2video' and audio_type='url'.
video_urlNoURL of the source video whose lip movements will be replaced. Provide either video_url or video_id.
audio_fileNoBase64-encoded audio file content. Required when mode='audio2video' and audio_type='file'.
audio_typeNoAudio source type. 'url' (default) to supply audio_url; 'file' to supply audio_file as a base64-encoded string.url
voice_speedNoSpeech speed multiplier from 0.8 to 2.0 (default 1.0). Used when mode='text2video'.
callback_urlNoWebhook URL that receives a POST when the lip-sync task completes.
voice_languageNoLanguage of the TTS voice. 'zh' for Chinese (default), 'en' for English. Used when mode='text2video'.zh

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changedv0.1.23
    • removedInput schema / properties / voice_speed / anyOf
      Removed value: -[
-  {
-    "maximum": 2,
-    "minimum": 0.8,
-    "type": "number"
-  },
-  {
-    "type": "null"
-  }
-]
    • changedInput schema / properties / voice_speed / default
      Previous value: -nullNew value: +1
    • addedInput schema / properties / voice_speed / maximum
      Added value: +2
    • addedInput schema / properties / voice_speed / minimum
      Added value: +0.8
    • addedInput schema / properties / voice_speed / type
      Added value: +"number"
  2. Changed2 schema fields changedv0.1.21
    • changedInput schema / properties / voice_speed / anyOf
      Previous value: -[
-  {
-    "type": "number"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "maximum": 2,
+    "minimum": 0.8,
+    "type": "number"
+  },
+  {
+    "type": "null"
+  }
+]
    • changedInput schema / properties / voice_speed / description
      Previous value: -"Speech speed multiplier (default 1.0). Used when mode='text2video'."New value: +"Speech speed multiplier from 0.8 to 2.0 (default 1.0). Used when mode='text2video'."
  3. Addedv0.1.20

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and mostly meets it: it explains the transformation (replacing speaker lip movements), the two modes, the TTS behavior in text2video mode, and the return value. It does not explicitly state that the source video is not modified or describe the asynchronous task workflow, but the returned Task ID strongly implies a new task/output.

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 organized well: a summary sentence, mode explanation, use-case bullets, and return value. It is not overly long despite covering a fairly rich tool, though the first and second sentences are mildly redundant.

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 11 parameters and no annotations, the description provides enough high-level context to correctly select and start using the tool. It would be stronger if it noted the async task nature and the need to poll kling_get_task or use callback_url, but the schema and output schema cover the detailed parameter relationships.

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 the baseline is 3. The description adds a small amount of extra meaning by noting that the source video can be supplied 'by URL or task ID' and that text2video generates audio via TTS, but it does not meaningfully deepen understanding beyond the 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 defines a specific verb+resource: it synchronizes lip movements in an existing video to match audio or text. It clearly distinguishes itself from siblings by emphasizing the video lip-sync task, including the URL/task-ID source and mode-based behavior.

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 'Use this when' bullets provide concrete, decision-relevant cases: dubbing, replacing generated-video audio, and creating talking-head videos from text. It does not explicitly state when to use a sibling tool instead, but the listed use cases are strong enough guidance for most selection scenarios.

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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