Pepys MCP vs QuillHub — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Pepys MCP vs QuillHub
In-depth architectural comparison of the Pepys MCP and QuillHub 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
Pepys MCP
Speech-to-Text · Local stdio
Quality: 59/100 (Good) | Auth: API Key required
QuillHub
Speech-to-Text · Remote HTTP/SSE
Quality: 52/100 (Good) | Auth: No auth required
Verdict Summary: Choose Pepys MCP if you need specialized Speech-to-Text tools running via a local process. Choose QuillHub if your workspace requires Speech-to-Text integration with remote web transport. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
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Choose Pepys MCP when:
You need dedicated capabilities in the Speech-to-Text domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (Freemium).
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.
Meeting transcripts for AI agents: search calls, read who said what, transcribe files and links.
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
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.
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).
Pepys MCP is categorized under Speech-to-Text and uses a local stdio subprocess. In contrast, QuillHub belongs to Speech-to-Text using remote streaming HTTP/SSE transport. Select Pepys MCP when you need capabilities focused on speech-to-text and QuillHub when you require tools for speech-to-text.
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.
QuillHub Tools (16)
search_meeting_transcripts
Full-text search across meetings in a workspace: titles, summaries, decisions, action items and transcript text. Morphological matching in English and Russian.
list_transcriptions
Paginated list of recordings with summary, key theses, decisions, action items, participants, project. Filter by text, date range, workspace, project.
get_transcription
One recording in the format you need: `summary`, `text`, `segments`, `dialog` (who said what), `paragraphs`, `chapters`, `subtitles`. Supports time windows for long calls.
get_person_speech
Timestamped quotes from one person across every meeting, with filters by date and project.
find_subjects
Find tracked subjects (people, deals, projects, clients, candidates, custom types) with their AI-maintained note, open commitments and mention counts.
get_subject_page
Full page for one subject: living note, open commitments, every meeting mention with quotes and speakers, unresolved questions.
get_state_timeline
How a subject's note changed over time: revisions with diffs and the recording that caused each change.
list_open_questions
Contradictions and ambiguities the AI found across meetings (e.g. two different budget figures) that need a human decision.
get_project_memory
AI-maintained briefing for a project or workspace: purpose, glossary, key people, activity digest.
list_subject_types
Subject types available in a project and the fields each one tracks.
list_workspaces
Your workspaces and the projects inside them, so the agent can read teammates' shared recordings.
create_transcription
Transcribe a URL (YouTube, direct file link) or an inline base64 file up to 25 MB.