ffmpeg-mcp-video-editor

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Translate speech to English

translate_transcript

Transcribe non-English speech from audio or video and translate it into English, optionally generating an English SRT subtitle file.

Instructions

Transcribe non-English speech and translate it into English.

This uses Whisper's built-in translate mode, which only ever outputs English — Whisper cannot translate into any other target language. To reach a different language you would need a separate translation step applied to the transcript this returns.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "input_path": "<input_path>"
    },
    "name": "translate_transcript"
  }
}

Input Schema

Name	Required	Description
`options`	No
`srt_path`	No	Also write an English SRT file.
`input_path`	Yes	Audio or video file containing non-English speech.

Output Schema

Name	Required	Default
`tool`	Yes
`job_id`	Yes
`status`	No	queued
`message`	No	Job queued. Poll job_status for progress, then job_result.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 6, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations provide minimal behavioral hints (readOnlyHint false, destructiveHint false), so the description carries the burden. It discloses a key non-obvious trait: Whisper's translate mode always outputs English and cannot translate to other languages. This adds valuable context beyond the structured annotations.

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 concise, with a clear first sentence stating the purpose and a second sentence adding a crucial limitation. No redundant information or fluff; every sentence earns its place.

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 the tool's complexity (an options object with multiple fields) and the presence of an output schema, the description provides the essential context: it handles non-English speech, outputs English, and notes the limitation. This is sufficient for an agent to select and invoke the tool correctly, though it does not detail return structures, which is acceptable since an output schema exists.

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?

The schema already provides descriptions for most parameters, including input_path and srt_path, and the options object has detailed field descriptions. The description does not add per-parameter semantics beyond noting the general purpose, so it meets the baseline for schema-covered parameters.

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 tool transcribes non-English speech and translates it into English, using a specific verb and resource. This distinguishes it from sibling tools like transcribe_audio or auto_caption by focusing on the translation-to-English aspect.

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 description explicitly explains when to use this tool (for non-English speech) and provides an alternative for other target languages (a separate translation step). It lacks an explicit comparison to transcribe_audio or auto_caption but the context makes the intended use clear.

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