In-depth architectural comparison of the MCP Listen 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
MCP Listen
Speech-to-Text · Local stdio
Quality: 56/100 (Good) | Auth: No auth required
QuillHub
Speech-to-Text · Remote HTTP/SSE
Quality: 52/100 (Good) | Auth: No auth required
Verdict Summary: Choose MCP Listen 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?
Choose MCP Listen 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).
Capture, transcribe (whisper.cpp), and query a local LLM (Ollama)
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.
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).
MCP Listen 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 MCP Listen when you need capabilities focused on speech-to-text and QuillHub when you require tools for speech-to-text.
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.