audiototext-mcp
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AlicenseAqualityDmaintenanceTranscribes audio files by referencing them in chat, using OpenAI's speech-to-text models locally without uploading audio, and supports speaker diarization.1MIT- AlicenseAqualityCmaintenanceEnables MCP clients to transcribe audio/video files locally, generate SRT subtitles, and burn captions into videos via tool calls, without a cloud API.3MIT
- FlicenseAqualityDmaintenanceEnables high-quality transcription and subtitle generation from local media files or URLs using Faster Whisper on local hardware. It supports automatic language detection and integration with MCP clients for seamless speech-to-text workflows.3-
- AlicenseNot gradedqualityCmaintenanceEnables local transcription of audio/video files and YouTube URLs, generation of SRT/VTT subtitles, and analysis of speech pacing and audience retention risk using faster-whisper.MIT
- 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
- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to transcribe local audio or video files on-device, with optional timestamps and cleanup, and to search locally stored dictation history without uploading audio or needing an API key. It also lets clients pre-download and verify the speech model before the first transcription.MIT
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion. The tool's purpose is clear and distinct by default.
The single tool name 'transcribe_file' follows a clear verb_noun convention. With only one tool, there is no naming inconsistency to evaluate.
A single tool is slightly below the typical 3-15 range, but it is well-scoped for a dedicated audio-to-text server. The tool fully addresses the server's narrow purpose without unnecessary additions.
The tool covers the complete transcription workflow: accepts local audio or video files, returns transcript with timestamps, and supports both local Whisper and OpenAI API backends. There are no obvious missing operations for this domain.