A
licenseNot graded
qualityB
maintenanceEnables AI agents to submit and manage durable long-form audio transcription jobs, and read or export structured transcripts via MCP tools.
1
MIT
Whisper Valet
by yashkotha
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TDQS
A3.9/5.0
Scored across 9 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: folder queueing, single-file pipeline, status, clip listing, transcript retrieval, report retrieval, configuration, quick transcription, and model listing. While process_audio and transcribe_quick both handle audio, their descriptions draw a clear line between full pipeline and plain synchronous transcription.
Naming Consistency4/5
Most tools follow a verb_noun pattern (process_folder, list_clips, get_transcript, list_models), but a few deviate: 'status' is a bare noun, 'configure' is a bare verb, and 'transcribe_quick' uses an adjective modifier instead of a noun. This is readable and close to consistent, but not perfectly uniform.
Tool Count5/5
With 9 tools, the set is well-scoped for a transcription pipeline. Each tool fills a necessary role in the workflow—from intake (process_*) to monitoring (status) to output retrieval (get_*)—without redundancy or bloat.
Completeness4/5
The core workflow is fully covered: queueing, processing, status, results, and configuration. Missing are management operations like deleting/canceling clips or controlling the watcher, but these are optional extras rather than gaps that would break the primary use case.
Maintenance
ActivityStale
ResponsivenessNo issues