meeting-transcriber-mcp
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Alternatives to meeting-transcriber-mcp
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- AlicenseAqualityAmaintenanceEnables transcription, summarization, and action item extraction from audio files on your Mac using MacWhisper and Claude Desktop, all locally without any cloud APIs.752 PyPI3MIT

digisensus-recorderofficial
AlicenseNot gradedqualityAmaintenanceLets AI agents search, read and export call recordings, transcripts and AI meeting notes from Digisensus Recorder, a free open-source call recorder for macOS, and start or stop recording. Runs locally on the Mac and talks to the app over a Unix socket.18GPL 3.0- AlicenseNot gradedqualityAmaintenanceExposes local, fully offline transcription with speaker diarization, voice-based speaker relabeling, environment diagnostics, and read-only workspace search as MCP tools for clients such as Claude Code and Claude Desktop. All audio, text, and voice embeddings stay on the user's Apple Silicon Mac, with no accounts, API keys, or cloud calls.300 PyPI7MIT
- AlicenseAqualityFmaintenanceEnables AI agents to interact with the ParrotScribe transcription service on macOS, providing tools to start/stop transcription, retrieve real-time and historical transcripts, and search across sessions.81 npmMIT
- FlicenseNot gradedqualityAmaintenanceGives your AI a live, speaker-labeled transcript of the meeting or call happening right now, plus the ability to push advice into the meeting window and speak out loud on the Mac. Requires the VoxAI macOS app — the server reads and writes that app's local files, so tools only return real data on macOS.-

TypeWhisper MCPofficial
AlicenseAqualityDmaintenanceConnects to the TypeWhisper macOS app to let coding agents transcribe local files, inspect model status, search history, and manage dictionary terms and corrections.105 npm3GPL 3.0
TDQS
Scored across 8 tools
Most tools have clear distinct purposes: the naming cluster (get_naming/confirm_naming/skip_naming) and the watch cluster (set_watch/get_watch_status) are separable, and get_job is unique. transcribe_file vs enqueue_files share the same transcription goal and differ mainly in blocking behavior, which is the one spot an agent could misselect, though descriptions clarify it well.
All tools follow a consistent snake_case verb_noun pattern (transcribe_file, enqueue_files, get_job, set_watch, get_naming, confirm_naming, skip_naming, get_watch_status). The get_/set_/confirm_/skip_ prefixes are used predictably.
Eight tools is well-scoped for a transcription-and-watch domain, with no redundant or filler endpoints. Each tool maps to a distinct capability (sync/async transcription, job polling, naming resolution, watch control and status).
The surface covers the core lifecycle: submit, queue, poll, resolve naming, and watch automation, and get_job returns transcript results. Minor gaps exist (no job cancellation, no list/enumerate jobs, no speaker enrollment), but the descriptions explicitly flag some of these as intentional and workflows remain achievable.