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macwhisper-mcp-server

macwhisper-mcp-server

by docdyhr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
MACWHISPER_CLINoPath to the mw binary. Defaults to /Applications/MacWhisper.app/Contents/MacOS/mw if that file exists, otherwise mw on PATH
MACWHISPER_LOG_PATHNoLog file path (never stdout — that's reserved for MCP)~/Library/Logs/macwhisper-mcp.log
MACWHISPER_ALLOWED_PATHSNoColon-separated list of directories the server may read from~/Desktop

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": true
}
resources
{
  "subscribe": false,
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
transcribe_audioA

Transcribe a local audio file and return the transcript.

IMPORTANT: path must be a file on the user's Mac filesystem inside the configured allow-list (typically ~/Desktop or ~/Downloads). Files uploaded to the Claude chat window are NOT accessible — ask the user to save the file to their Desktop or Downloads folder first.

If this tool returns an access-denied error, do NOT attempt to transcribe the file by any other means (e.g. downloading a model, calling an external API, or using in-process speech recognition). Simply tell the user to save the file locally and retry with this tool.

cancel_transcriptionA

Cancel the currently running transcription, if any.

list_modelsA

Return the transcription models available across all engines.

MacWhisper entries are formatted as engine:model-id — Display Name [active] where [active] marks the model currently selected in MacWhisper; pass the engine:model-id string as model with the default engine="macwhisper". whisper-cpp entries (if MACWHISPER_WHISPERCPP_MODEL_DIR is configured) are formatted as filename [whisper-cpp]; pass the filename as model with engine="whisper-cpp". Requires the MacWhisper CLI to be reachable even if you only intend to use the whisper-cpp engine.

list_allowed_pathsA

Return the directories this server is allowed to read audio from.

start_watchA

Start watching a folder for new audio files to auto-transcribe.

New audio files dropped into folder are transcribed automatically and moved to a "done" directory. By default this is <folder>/../done; override it with the MACWHISPER_WATCH_DONE_DIR env var. Both the incoming folder and the done directory must be inside the configured allow-list, otherwise the call is rejected. Call get_watch_results() to retrieve completed transcriptions.

stop_watchB

Stop the active folder watcher.

get_watch_resultsA

Return completed watch-folder transcriptions and clear the queue.

Each entry contains: file, transcript, destination, error.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored across 7 tools

Disambiguation5/5

Each tool targets a distinct action: single-file transcription, cancellation, model listing, path listing, and the watch lifecycle (start/stop/results). The watch tools and direct transcription tools have clearly separated purposes with no overlap. An agent can easily tell them apart.

Naming Consistency5/5

All seven tools follow a consistent snake_case verb_noun pattern (transcribe_audio, cancel_transcription, list_models, list_allowed_paths, start_watch, stop_watch, get_watch_results). Verb choices are precise and predictable.

Tool Count5/5

Seven tools is well-scoped for a focused transcription server. Every tool earns its place: core transcription, lifecycle control, discovery helpers, and the watch-folder feature set.

Completeness4/5

Core lifecycle is covered (transcribe, cancel, watch start/stop/results, model and path discovery). Minor gap: there is no status/progress query for an in-flight transcription beyond cancel, but agents can reasonably work around this.

Maintenance

ActivityActive
ResponsivenessSyncing

Resources