What can you do with this server?

This MCP server transcribes audio to text using either a local faster-whisper model or the OpenAI Whisper API, with optional GPT-powered post-processing. Transcribe audio files (transcribe_file): Convert audio files (mp3, wav, m4a, ogg, flac, webm, etc.) to text by providing an absolute file path. Transcribe base64-encoded audio (transcribe_base64): Accept audio as a base64-encoded string for programmatic integrations. Both transcription tools support: * Optional language specification (e.g., en, es) or auto-detection * Choice of local model size (tiny, base, small, medium, large-v3) * Optional GPT-4.1 post-processing to fix spelling, grammar, and punctuation * Custom post-processing prompts for domain-specific terminology or proper nouns List models and configuration (list_models): View the active backend, available local model sizes, and the GPT model used for post-processing. Backend options: * Local (faster-whisper): Free, works fully offline after model download * OpenAI Whisper API: Cloud-based, requires an API key * Both: OpenAI is prioritized when an API key is present, with automatic fallback to local if the API call fails Compatible with Claude Desktop and Claude Code on macOS, Windows, and Linux.

Which integrations are available for this server?

Provides audio transcription using OpenAI's Whisper API, requiring an API key and internet connection.

How do I use whisper-transcribe-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@whisper-transcribe-mcp transcribe the file meeting.mp3" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

whisper-transcribe-mcp

by ZahiriNatZuke

Overview Schema Related Servers Score Discussions

Python

Local

whisper-transcribe-mcp

PyPI version License: MIT Python 3.10+

MCP server for audio transcription using faster-whisper (local, free, offline) or OpenAI Whisper API (cloud, requires API key). Works with Claude Desktop and Claude Code on macOS, Windows, and Linux.

Prerequisites

macOS

Option A — uv (recommended):

brew install uv
# or
curl -LsSf https://astral.sh/uv/install.sh | sh

Option B — Python: Python 3.10+ is included in macOS 12.3+. You can also install it with brew install python.

Windows

Option A — uv (recommended):

winget install astral-sh.uv

Or download the installer from astral.sh/uv.

Option B — Python: Download Python 3.10+ from python.org. During installation, check "Add Python to PATH".

No need to install ffmpeg or any compiler — everything is bundled in the package.

Linux

Option A — uv (recommended):

curl -LsSf https://astral.sh/uv/install.sh | sh

Option B — Python:

# Debian/Ubuntu
sudo apt install python3.12 python3.12-venv

# Fedora
sudo dnf install python3.12

# Arch
sudo pacman -S python

No additional system dependencies required.

Related MCP server: audio-transcription-mcp

Installation

Option A — uvx (recommended, no permanent install)

uvx automatically downloads and installs the package in an isolated environment. Only requires uv to be installed.

# Local backend:
uvx "whisper-transcribe-mcp[local]"

# OpenAI backend:
uvx "whisper-transcribe-mcp[openai]"

# Both backends:
uvx "whisper-transcribe-mcp[all]"

Option B — pip

# Local backend:
pip install "whisper-transcribe-mcp[local]"

# OpenAI backend:
pip install "whisper-transcribe-mcp[openai]"

# Both backends:
pip install "whisper-transcribe-mcp[all]"

Use Cases

Case 1 — Local backend only (free, works offline)

Uses faster-whisper to transcribe locally. The model is downloaded from HuggingFace on first use (~74MB for base) and cached.

Install:

pip install "whisper-transcribe-mcp[local]"

Environment variables:

WHISPER_MODEL=base   # or tiny, small, medium, large-v3

Case 2 — OpenAI backend only (best accuracy, requires API key)

Uses OpenAI's whisper-1 model. Requires an API key and internet connection. No local model downloads.

Install:

pip install "whisper-transcribe-mcp[openai]"

Environment variables:

OPENAI_API_KEY=sk-...

Case 3 — Both backends (OpenAI if key present, local as fallback)

If OPENAI_API_KEY is set, OpenAI is used automatically. Otherwise falls back to local faster-whisper.

Install:

pip install "whisper-transcribe-mcp[all]"

Configuration

Claude Desktop

Config file location by operating system:

OS	Path
macOS	`~/Library/Application Support/Claude/claude_desktop_config.json`
Windows	`%APPDATA%\Claude\claude_desktop_config.json`
Linux	`~/.config/Claude/claude_desktop_config.json`

Add the entry inside "mcpServers":

Windows note: Claude Desktop runs in a restricted environment and may not have uvx in its PATH, and it may use a Python version (e.g. 3.14) for which ctranslate2 (a dependency of faster-whisper) does not yet have prebuilt wheels. Two fixes are required:
Use the full path to uvx.exe instead of just uvx. Run where.exe uvx in PowerShell to find it (usually C:\Users\<YourUser>\.local\bin\uvx.exe).
Force Python 3.12 via the --python 3.12 flag so that a compatible wheel is used.

Case 1 — Local:

macOS / Linux:

{
  "mcpServers": {
    "whisper-transcribe": {
      "command": "uvx",
      "args": ["whisper-transcribe-mcp[local]"],
      "env": {
        "WHISPER_MODEL": "base"
      }
    }
  }
}

Windows:

{
  "mcpServers": {
    "whisper-transcribe": {
      "command": "C:\\Users\\<YourUser>\\.local\\bin\\uvx.exe",
      "args": ["--python", "3.12", "whisper-transcribe-mcp[local]"],
      "env": {
        "WHISPER_MODEL": "base"
      }
    }
  }
}

Case 2 — OpenAI:

macOS / Linux:

{
  "mcpServers": {
    "whisper-transcribe": {
      "command": "uvx",
      "args": ["whisper-transcribe-mcp[openai]"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Windows:

{
  "mcpServers": {
    "whisper-transcribe": {
      "command": "C:\\Users\\<YourUser>\\.local\\bin\\uvx.exe",
      "args": ["--python", "3.12", "whisper-transcribe-mcp[openai]"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Case 3 — Both (OpenAI takes priority if key is set):

macOS / Linux:

{
  "mcpServers": {
    "whisper-transcribe": {
      "command": "uvx",
      "args": ["whisper-transcribe-mcp[all]"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "WHISPER_MODEL": "base"
      }
    }
  }
}

Windows:

{
  "mcpServers": {
    "whisper-transcribe": {
      "command": "C:\\Users\\<YourUser>\\.local\\bin\\uvx.exe",
      "args": ["--python", "3.12", "whisper-transcribe-mcp[all]"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "WHISPER_MODEL": "base"
      }
    }
  }
}

Restart Claude Desktop after editing the file.

Claude Code

Works the same on macOS, Windows, and Linux. Requires uv installed.

Claude Code config file location:

OS	Global	Per project
macOS / Linux	`~/.claude.json`	`.claude/settings.json` (project root)
Windows	`C:\Users\<user>\.claude.json`	`.claude\settings.json` (project root)

The easiest way to add the server is via the Claude Code CLI, which updates the config file automatically:

# Case 1 — Local:
claude mcp add whisper-transcribe uvx -- "whisper-transcribe-mcp[local]"

# Case 2 — OpenAI:
claude mcp add whisper-transcribe uvx --env OPENAI_API_KEY=sk-... -- "whisper-transcribe-mcp[openai]"

# Case 3 — Both (OpenAI with local fallback):
claude mcp add whisper-transcribe uvx --env OPENAI_API_KEY=sk-... --env WHISPER_MODEL=base -- "whisper-transcribe-mcp[all]"

Windows + [all]: Add --python 3.12 before the package name to avoid ctranslate2 wheel issues. Edit ~/.claude.json directly and use "args": ["--python", "3.12", "whisper-transcribe-mcp[all]"].

To add it globally (available in all projects), use --scope user:

claude mcp add --scope user whisper-transcribe uvx -- "whisper-transcribe-mcp[local]"

Or edit ~/.claude.json directly and add inside "mcpServers":

{
  "mcpServers": {
    "whisper-transcribe": {
      "command": "uvx",
      "args": ["whisper-transcribe-mcp[local]"],
      "env": {
        "WHISPER_MODEL": "base"
      }
    }
  }
}

Environment Variables

Variable	Default	Description
`WHISPER_MODEL`	`base`	Local model size: `tiny`, `base`, `small`, `medium`, `large-v3`
`OPENAI_API_KEY`	—	If set, activates the OpenAI backend instead of local

Backend selection and fallback (`[all]` only)

When installed with [all], the backend is chosen at startup:

OPENAI_API_KEY set → OpenAI is used. If the API call fails at runtime (network error, invalid key, quota exceeded), the server automatically falls back to local faster-whisper and includes a "fallback_reason" field in the response.
OPENAI_API_KEY not set → local faster-whisper is used directly, no fallback attempted.

Available Tools

`transcribe_file`

Transcribes an audio file by path (mp3, wav, m4a, ogg, flac, webm, etc.).

Parameters:

file_path (required): Absolute path to the audio file
language (optional): Language code (es, en, fr, etc.). Auto-detected if not provided.
model_size (optional): Local model size. Ignored with the OpenAI backend.
post_process (optional, default false): If true, passes the transcription through GPT-4.1 to fix spelling, grammar, and punctuation. Requires the openai package ([openai] or [all]).
post_process_prompt (optional): Custom system prompt for GPT post-processing. Use it to provide domain-specific context, proper nouns, or product names that Whisper may have misspelled. Falls back to a generic correction prompt if not provided.

Response (without post-processing):

{
  "text": "Full transcription...",
  "language": "en",
  "language_probability": 0.99,
  "segments": [
    { "start": 0.0, "end": 4.2, "text": "First segment..." }
  ],
  "backend": "local",
  "model": "base"
}

Response (with post_process: true):

{
  "text": "Corrected transcription...",
  "raw_text": "Original transcription from Whisper...",
  "post_process_model": "gpt-4.1",
  "language": "en",
  "language_probability": 0.99,
  "segments": [...],
  "backend": "local",
  "model": "base"
}

If post-processing fails, text retains the original transcription and a post_process_error field is added.

`transcribe_base64`

Transcribes audio provided as a base64-encoded string. Useful for programmatic integrations.

Parameters:

audio_base64 (required): Base64-encoded audio data
extension (optional, default mp3): File extension (mp3, wav, ogg, etc.)
language (optional): Language code
model_size (optional): Local model size
post_process (optional, default false): Same as in transcribe_file.
post_process_prompt (optional): Same as in transcribe_file.

`list_models`

Shows the active backend configuration, available local models, and the GPT model used for post-processing.

Local Model Sizes

Model	Size	Relative Speed	Notes
`tiny`	39 MB	~32x	Fastest, least accurate
`base`	74 MB	~16x	Good balance (default)
`small`	244 MB	~6x	Better accuracy
`medium`	769 MB	~2x	High accuracy
`large-v3`	1.5 GB	~1x	Best accuracy, slowest

Models are downloaded automatically from HuggingFace on first use and cached locally.

Troubleshooting

MCP not loading in Claude Desktop on Windows

Symptom: The server fails to start with a dependency resolution error like:

ctranslate2>=4.6.1 has no wheels with a matching platform tag (e.g., `win32`)
hint: You require CPython 3.14 (`cp314`), but we only found wheels for `ctranslate2` with: `cp39`, `cp310`, `cp311`, `cp312`, `cp313`

Cause: Two issues combined:

Claude Desktop does not include the user's local bin in its PATH, so uvx must be referenced by full path.
Claude Desktop's uvx may pick a Python version (e.g. 3.14) for which ctranslate2 — a native dependency of faster-whisper — does not yet have prebuilt wheels for Windows.

Fix: Use the full path to uvx.exe and force Python 3.12 explicitly:

"whisper-transcribe": {
  "command": "C:\\Users\\<YourUser>\\.local\\bin\\uvx.exe",
  "args": ["--python", "3.12", "whisper-transcribe-mcp[local]"],
  "env": { "WHISPER_MODEL": "base" }
}

To find your exact uvx.exe path, run in PowerShell:

where.exe uvx

Transcribing audio files in Claude Desktop

Symptom: Claude Desktop fails to transcribe an uploaded audio file. It may attempt to read the file as base64 and pass it to transcribe_base64, which then fails or hangs for files larger than ~50 KB.

Cause: Claude Desktop runs in a sandboxed Linux container. When you upload a file using the attachment button, it is stored at a path like /mnt/user-data/uploads/audio.mp3 — inside the container. The MCP server runs on your Windows machine and has no access to that container path. Claude's fallback of base64-encoding the file and passing it to transcribe_base64 fails in practice because even a small audio file produces hundreds of kilobytes of base64 text, which overflows the context window before the tool call can be made.

Fix: Do not use the attachment button to upload audio files. Instead, place the file anywhere on your Windows filesystem and reference its path directly in the message:

"Transcribe the file at C:\Users\YourUser\Downloads\audio.mp3"

The MCP server will read the file directly from Windows and send it to the transcription backend. This works for files of any size within the Whisper API limit (25 MB).

Development

This project uses uv for reproducible development environments:

uv sync --group dev
uv run ruff check .
uv run ruff format --check .
uv run pytest -q
uv run pre-commit install

See CONTRIBUTING.md for the contribution workflow.

Distribution and releases

The package is distributed through PyPI and described by server.json for the official MCP Registry. Version tags publish both destinations through GitHub OIDC, without long-lived publishing tokens. See docs/publishing.md for the release checklist and one-time repository setup.

The Registry entry describes the base PyPI package. Choose the [local], [openai], or [all] extra from the installation examples above so the transcription backend you need is installed.

License

MIT — see LICENSE

Available Tools

3 tools

list_modelsA

List available Whisper model sizes and current configuration.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A3.9/5.0

Behavior2/5

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

No annotations provided; description states it lists information but does not explicitly confirm read-only or side effects. Minimal 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?

Single sentence with zero waste; front-loaded with action and resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No parameters, output schema exists to describe return values; description fully covers purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters in schema (0 count), baseline is 4. Description adds no param info, but none needed.

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?

Clear verb 'List' and specific resource 'available Whisper model sizes and current configuration'. Distinguishes from sibling tools transcribe_base64 and transcribe_file.

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?

Description implies usage for checking available models, but no explicit when-to-use or alternatives given.

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

transcribe_base64B

Transcribe audio provided as a base64-encoded string.

ParametersJSON Schema

Name	Required	Description	Default
`language`	No	Language code. Auto-detected if not provided.
`extension`	No	File extension for the temp file (mp3, wav, m4a, ogg, etc.).	mp3
`model_size`	No	Local model size. Ignored when using the OpenAI backend.
`audio_base64`	Yes	Base64-encoded audio data.
`post_process`	No	If True, passes the transcription through GPT to fix spelling, grammar, and punctuation. Requires the openai package.
`post_process_prompt`	No	Custom system prompt for post-processing. Use this to provide domain-specific context, proper nouns, or product names.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral details such as backend used, internet requirement, output format, or limitations (e.g., audio length), relying solely on minimal description.

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 a single, clear sentence with no wasted words, efficiently conveying the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite a rich input schema and available output schema, the description omits essential context such as return format, prerequisites, and backend behavior, making it incomplete for complex tool usage.

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 description adds no additional meaning beyond what the input schema already provides; baseline score of 3 is appropriate.

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 audio from a base64-encoded string, distinguishing it from siblings like transcribe_file (file-based) and list_models.

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?

The description implies usage for base64 audio input but lacks explicit when-to-use, when-not-to-use, or alternative guidance, leaving room for confusion with transcribe_file.

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

transcribe_fileB

Transcribe an audio file to text.

ParametersJSON Schema

Name	Required	Description
`language`	No	Language code (e.g. 'es', 'en', 'fr'). Auto-detected if not provided.
`file_path`	Yes	Absolute path to the audio file (mp3, wav, m4a, ogg, flac, etc.)
`model_size`	No	Local model size: tiny, base, small, medium, large-v3. Ignored when using the OpenAI backend. Defaults to the WHISPER_MODEL environment variable (default: 'base').
`post_process`	No	If True, passes the transcription through GPT to fix spelling, grammar, and punctuation. Requires the openai package.
`post_process_prompt`	No	Custom system prompt for post-processing. Use this to provide domain-specific context, proper nouns, or product names that Whisper may have misspelled. Falls back to a generic correction prompt if not provided.

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

B3.1/5.0

Behavior2/5

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

The description does not disclose any behavioral traits beyond the basic action. It does not mention if the tool requires network access, writes temporary files, uses local or remote processing, or any error conditions. Since no annotations are provided, the description carries the full burden but fails to provide meaningful 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that is immediately clear. It is appropriately front-loaded but could benefit from minimal structured details (e.g., supported formats) without becoming verbose. The extreme brevity does not hurt clarity but leaves some gaps.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the presence of an output schema and full parameter documentation, the description fails to provide context about when to use this tool vs. the sibling 'transcribe_base64', or about any side effects or limitations. For a tool with five parameters and multiple options, the description is insufficiently 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?

The input schema has 100% description coverage, so the schema already documents all five parameters thoroughly. The tool description adds no additional semantic value beyond what is in the schema, resulting in a baseline score of 3.

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 action ('transcribe') and the resource ('audio file'), immediately distinguishing the tool from its siblings 'list_models' (which lists models) and 'transcribe_base64' (which transcribes base64-encoded audio). The verb+resource combination is specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its siblings or alternatives. It does not mention file size limits, audio duration constraints, or any prerequisites. Users receive no contextual advice on selecting this tool over transcribe_base64 or list_models.

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

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.7/5.0

Disambiguation5/5

Each tool serves a distinct purpose: listing models, transcribing from base64, and transcribing from file. No overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case (list_models, transcribe_base64, transcribe_file).

Tool Count5/5

Three tools is well-scoped for a transcription server: one for configuration/model listing and two for handling different input formats (base64 vs file).

Completeness4/5

The tool surface covers the core transcription workflow with two input methods and model listing. Minor gap: no support for streaming or URL-based audio input.

Maintenance

ActivitySlowing

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

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whisper-transcribe-mcp

Prerequisites

macOS

Windows

Linux

Installation

Option A — uvx (recommended, no permanent install)

Option B — pip

Use Cases

Case 1 — Local backend only (free, works offline)

Case 2 — OpenAI backend only (best accuracy, requires API key)

Case 3 — Both backends (OpenAI if key present, local as fallback)

Configuration

Claude Desktop

Claude Code

Environment Variables

Backend selection and fallback ([all] only)

Available Tools

transcribe_file

transcribe_base64

list_models

Local Model Sizes

Troubleshooting

MCP not loading in Claude Desktop on Windows

Transcribing audio files in Claude Desktop

Development

Distribution and releases

License

Available Tools

Output Schema

Output Schema

Output Schema

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API

Backend selection and fallback (`[all]` only)

`transcribe_file`

`transcribe_base64`

`list_models`