pepys-mcp
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- AlicenseAqualityBmaintenanceEnables AI assistants to transcribe audio and video from URLs or local files with high accuracy, speaker diarization, 119 languages, and word-level timestamps, while also supporting transcription management and caption export in SRT, WebVTT, or plain text.1493 npm13MIT
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- FlicenseAqualityCmaintenanceEnables an assistant to transcribe video or podcast recordings, then read, search, summarize, and manage the resulting transcripts through MCP tools.10-
- AlicenseNot gradedqualityAmaintenanceEnables transcription and speaker diarization of audio files, interviews, and YouTube URLs, producing speaker-attributed transcripts with timestamps. Supports multiple backends (local Whisper, OpenAI API) and output formats (txt, vtt, srt, json).Apache 2.0
- AlicenseAqualityFmaintenanceEnables AI assistants to transcribe audio files from URLs or local paths using AssemblyAI's services, with support for speaker diarization, language detection, and asynchronous job management through a standardized MCP interface.419 npm2MIT
TDQS
Scored across 9 tools
Each tool targets a distinct operation: uploading, transcribing (single/batch), fetching results, exporting, searching, listing, and credit checking. No functional overlap exists, allowing clear selection.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., get_transcription, list_transcriptions, upload_file). The verb transcribe is used alone for the primary action, but its derived tools (transcribe_podcast_feed) remain consistent.
With 9 tools, the set is well-scoped for a transcription server. Each tool serves a distinct and necessary purpose without redundancy, fitting the typical optimal range of 3-15 tools.
The tool set covers the full transcription workflow: upload, transcribe (single and batch), fetch, export, search, and credit management. The only minor gap is the absence of a delete/reset tool, but this is not essential for the core use case.