A Model Context Protocol (MCP) server for advanced audio transcription and processing using OpenAI's Whisper and GPT-4o models.
Most AI and media services work still happens through a UI a human drives. Whisper MCP server moves it into the conversation instead. A Model Context Protocol (MCP) server for advanced audio transcription and processing using OpenAI's Whisper and GPT-4o models.
MCP Server Whisper provides a standardized way to process audio files through OpenAI's latest transcription and speech services. By implementing the Model Context Protocol, it enables AI assistants like Claude to seamlessly interact with audio processing capabilities.
The server publishes 12 tools. What each one is for:
min_duration_seconds — 300 (5 minutes)min_modified_time — <timestamp for Jan 1, 2024>sort_by — "size"voice — "shimmer"model — "gpt-4o-mini-tts" (default high-quality model)instructions — "Speak in an enthusiastic, podcast host style" (optional)speed — 1.0 (default, can be adjusted)Type — Safe Responses**: Every tool returns a strongly-typed Pydantic model (TranscriptionResult, ChatResult, AudioProcessingResult, TTSResult)Single — Item Operations**: One call processes one file, with MCP protocol handling parallelism nativelyPer — File Error Handling**: Failures are isolated to individual operations, not entire batchesSelf-Documenting — Type hints provide autocomplete and validation in IDEs and AI modelsTranscription — The Transcription tool exposed by this serverConfiguration is passed through the environment: OPENAI_API_KEY, AUDIO_FILES_PATH. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Whisper's toolset — min_duration_seconds, min_modified_time, sort_by and 9 more — is a fair guide to whether it matches your workflow. It is maintained by arcaputo3; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| min_duration_seconds | 300 (5 minutes) |
| min_modified_time | <timestamp for Jan 1, 2024> |
| sort_by | "size" |
| voice | "shimmer" |
| model | "gpt-4o-mini-tts" (default high-quality model) |
| instructions | "Speak in an enthusiastic, podcast host style" (optional) |
| speed | 1.0 (default, can be adjusted) |
| Type | Safe Responses**: Every tool returns a strongly-typed Pydantic model (TranscriptionResult, ChatResult, AudioProcessingResult, TTSResult) |
| Single | Item Operations**: One call processes one file, with MCP protocol handling parallelism natively |
| Per | File Error Handling**: Failures are isolated to individual operations, not entire batches |
| Self-Documenting | Type hints provide autocomplete and validation in IDEs and AI models |
| Transcription | The Transcription tool exposed by this server. |
{
"mcpServers": {
"whisper": {
"command": "uvx",
"args": ["mcp-server-whisper"],
"env": {
"OPENAI_API_KEY": "your_openai_api_key",
"AUDIO_FILES_PATH": "/path/to/your/audio/files"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| AUDIO_FILES_PATH | Filesystem location the server is allowed to use. | Optional |
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.
Thousands of open models on tap — run image, video and audio generation through Replicate's hosted API.