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by hermoso-ai
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JavaScript
Remote

Dub video

dub_video

Localize a finished video into another language without re-rendering. Transcribes, translates, re-voices and lip-syncs audio onto the same footage; pass video and language.

Instructions

Localize a finished video into another language WITHOUT re-rendering it: the spoken track is transcribed, translated, re-voiced and lip-synced back onto the SAME footage, so the visuals, timing and edit are untouched. Just pass the video and the language — the script is read off the source automatically (pass script only to override what it heard). Paid; returns the served URL of the localized video.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
videoYesthe source video URL
voiceNooptional target voice preset, e.g. 'Aria' (warm female) or 'George' (confident male). Defaults to a voice matching the source speaker's register.
scriptNoOPTIONAL override for the original spoken words. Leave this out — the source video is transcribed automatically. Only pass it when you already know the exact script and the auto-transcript got it wrong.
languageYestarget language, e.g. 'Spanish', 'de', 'French (Canada)'

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.1.161
    • changedInput schema / properties / script / description
      Previous value: -"the original spoken script if known — improves translation fidelity"New value: +"OPTIONAL override for the original spoken words. Leave this out — the source video is transcribed automatically. Only pass it when you already know the exact script and the auto-transcript got it wrong."
    • addedInput schema / properties / voice
      Added value: +{
+  "description": "optional target voice preset, e.g. 'Aria' (warm female) or 'George' (confident male). Defaults to a voice matching the source speaker's register.",
+  "type": "string"
+}
    • changedOutput schema / (root)
      Previous value: -{
-  "$schema": "http://json-schema.org/draft-07/schema#",
-  "additionalProperties": false,
-  "properties": {
-    "jobId": {
-      "description": "the render job id — poll get_job with this id to resume or inspect",
-      "type": "string"
-    },
-    "model": {
-      "anyOf": [
-        {
-          "type": "string"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "description": "the product-facing label of the model that rendered it"
-    },
-    "raw": {
-      "description": "the raw job result payload (e.g. images[] for carousel template ads)"
-    },
-    "stillRendering": {
-      "description": "true when the render is still in progress — keep polling get_job with jobId",
-      "type": "boolean"
-    },
-    "url": {
-      "anyOf": [
-        {
-          "type": "string"
-        },
-        {
-          "type": "null"
-        }
-      ],
-      "description": "the served URL of the finished media (absent/null while still rendering)"
-    }
-  },
-  "type": "object"
-}New value: +null
  2. Addedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare non-readonly, non-destructive, open-world behavior, but the description adds material context: the source footage is left untouched (nothing re-rendered), the transcription is automatic, and the operation is Paid. It also discloses the return value (served URL of the localized video). Cost and output disclosure go beyond the annotation set.

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?

Front-loads the differentiating trait (no re-rendering, visuals/timing/edit untouched) before mechanics and cost. Two dense sentences with no padding, and the routing-relevant 'just pass the video and the language' instruction is easy to find.

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?

With no output schema, the description helpfully states the return is the served URL of the localized video, and annotations cover the safety profile. The only gap is the absence of any routing against similar siblings, but for calling the tool correctly the definition is essentially 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 100%, so the schema already documents all four parameters, and the description largely restates them (script auto-read, voice default matching the speaker's register). With the schema doing the heavy lifting, baseline 3 is appropriate; no additional syntax or format detail is added.

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?

States a specific verb (localize/dub) and resource (a finished video) and explains the mechanism: transcription, translation, re-voicing and lip-sync onto the SAME footage without re-rendering. This distinguishes it from siblings like change_voice (voice only), add_subtitles (text only) and localize_ad (ads rather than finished videos).

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?

Gives clear invocation guidance ('just pass the video and the language') and an important scoping rule for `script` (only to override the auto-transcript). However it never states when to prefer this tool over nearby alternatives such as localize_ad or change_voice, and offers no exclusions, so usage is implied rather than explicit.

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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