Voicemode vs Pepys Mcp — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Voicemode vs Pepys Mcp
In-depth architectural comparison of the Voicemode and Pepys Mcp MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Voicemode
Speech-to-Text · Local stdio
Quality: 27/100 (Emerging) | Auth: No auth required
Pepys Mcp
Speech-to-Text · Local stdio
Quality: 63/100 (Good) | Auth: API Key required
Verdict Summary: Choose Voicemode if you need specialized Speech-to-Text tools running via a local process. Choose Pepys Mcp if your workspace requires Speech-to-Text integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
V
Choose Voicemode when:
You need dedicated capabilities in the Speech-to-Text domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Natural voice conversations for AI assistants - STT/TTS via MCP
Pay-once transcription for audio, video, and whole podcast feeds via Pepys. Transcribe a file or a pasted YouTube/podcast link, get speaker diarization, export SRT/VTT, search a transcript, and check credit balance. Hosted connector (OAuth, no API key) or npx pepys-mcp. 99+ languages.
No explicit tool names declared in metadata yet. Check project README on main listing page.
Pepys Mcp Tools (9)
transcribe
Transcribe hours-long audio or video into an accurate, speaker-labeled (diarized), timestamped transcript with correctly-timed SRT/VTT captions – work a general model can't do on a raw file. Accepts a file_ref from upload_file or a url (YouTube, podcast episode, RSS feed, Google Drive/Dropbox share). Audio is never used to train models. Returns { job_id, status }; fetch the result with get_transcription.
get_transcription
Fetch a transcription by job_id: full text, per-speaker timestamped segments, summary, duration_seconds, billed_minutes, and language. Set wait_ms (up to 25000) to long-poll so short clips come back in one call; otherwise poll until status is 'done'.
upload_file
Upload local audio/video the agent is holding (as base64 bytes or a file path) and get back a file_ref to pass to transcribe. Use this when the media has no public URL. Requires the Pepys R2 storage backend.
list_transcriptions
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Voicemode is categorized under Speech-to-Text and uses a local stdio subprocess. In contrast, Pepys Mcp belongs to Speech-to-Text using local stdio subprocess. Select Voicemode when you need capabilities focused on speech-to-text and Pepys Mcp when you require tools for speech-to-text.
List this account's recent transcription jobs with their job_id, status, title, and duration, so you can resume, fetch, or export an earlier result instead of re-transcribing.
list_podcast_episodes
Given a podcast RSS feed or Apple Podcasts show URL, list its episodes (title, publish date, episode_guid, audio_url) so you can pick exactly which one to transcribe.
transcribe_podcast_feed
Batch-transcribe a whole podcast feed in one call – fan out every episode, or the latest N, to individual jobs. Returns a set of job_ids. Paid capability (throughput/abuse gate).
export_transcript
Export a finished transcript as SRT, VTT, TXT, Markdown, or JSON, with correct caption timings. Segment-level export is free; word-level-timed export (word_level:true) is a paid unlock. (DOCX/PDF are available in the Pepys web app.)
search_transcript
Search inside a long transcript for a phrase and get back only the matching timestamped segments – locate a quote or topic in an hours-long recording without loading the whole transcript into context.
get_credit_balance
Return the account's remaining transcription credits (in minutes) so you can check headroom before starting a large batch and avoid running out mid-run.