Transcribe speech from video or audio files into text or SRT subtitles using Whisper, with optional language and model selection.
Transcribe speech to text using Whisper.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | base | |
| language | No | ||
| overwrite | No | Explicitly allow replacing existing media. Existing non-media files are protected. | |
| input_path | Yes | ||
| output_srt | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.16.4Input schema / properties / overwriteAdded value: +{
+ "default": false,
+ "description": "Explicitly allow replacing existing media. Existing non-media files are protected.",
+ "title": "Overwrite",
+ "type": "boolean"
+}v1.16.0Input schema / additionalPropertiesAdded value: +falsev1.5.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing about whether the tool writes files to disk, runs locally or via API, is idempotent, or what happens on an existing output path. The only behavioral hint ('existing non-media files are protected') comes from the schema's overwrite field, not the description itself.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with zero filler, which is well structured. The tradeoff is that it is under-sized for a five-parameter tool, but no sentence is wasted or buried.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists so return values needn't be explained, but for a five-parameter tool with 20% schema coverage and heavy sibling overlap, the description leaves the agent without enough to call it confidently or choose it over hyperframes_transcribe.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 20% (only 'overwrite' is documented), so the description needed to compensate and does not — it never mentions model choice, language, input_path, or output_srt. An agent cannot tell from the description that 'model' defaults to 'base' or that output_srt controls where captions are written.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Transcribe speech to text') plus the underlying engine ('using Whisper'), so an agent knows exactly what the tool produces. However, it gives no signal to distinguish it from close siblings like hyperframes_transcribe, video_generate_subtitles, or sound_qa_asr, which all claim overlapping speech-to-text territory.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this versus alternatives, no prerequisites, and no exclusions. With at least three sibling tools that sound like transcription, the absence of any routing hint leaves the agent to guess.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.