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Hermoso

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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: transcribe, translate, re-voice, and lip-sync the original footage. Pass the video URL and target 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

A4.4/5.0
Behavior5/5

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

The description richly discloses behavior beyond the sparse annotations: it explains the full pipeline (transcribe, translate, re-voice, lip-sync), notes the output is a served URL, and states the operation is paid. It also clarifies that the original footage is not re-rendered, which is significant behavioral context.

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 compact and front-loaded: the core value proposition appears first, followed by usage instructions, payment detail, and return type. Every sentence contributes new information without unnecessary fluff.

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 there is no output schema, the description appropriately mentions the return value (served URL) and cost (paid). It also covers the main input requirements and the optional script override. It could be slightly more complete by mentioning billing setup or prerequisites, but for this complexity level it is largely sufficient.

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 parameter guidance—'Just pass the video and the language' and 'pass script only to override'—but the input schema already documents these same semantics, so the added value is marginal.

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 verb 'localize' with the resource 'a finished video' and highlights the key distinction from re-rendering: the visual, timing, and edit remain untouched. This makes it easy to tell apart from siblings like edit_video, clip_video, or reframe_video.

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?

It gives clear context on when to use the tool: pass the video and target language, and only override the script when the auto-transcript is wrong. It doesn't explicitly name alternatives or state when not to use it, but the 'finished video' + 'no re-render' framing conveys the appropriate use case.

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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