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licenseA
qualityD
maintenanceThis MCP server enables audio transcription using OpenAI Whisper, supporting various model sizes and base64-encoded audio input via stdio or HTTP transport.
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whisper-telegram-mcp
by abid-mahdi
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TDQS
A4.2/5.0
Scored across 5 tools
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: check_backends verifies setup, list_models shows model options, speak_text converts text to speech, transcribe_audio processes local audio files, and transcribe_telegram_voice handles Telegram-specific downloads. The descriptions reinforce these distinct roles, making misselection unlikely.
Naming Consistency4/5
Tools follow a consistent snake_case pattern throughout (e.g., check_backends, list_models, speak_text). However, there is a minor deviation in verb style: 'check' and 'list' are informational, while 'speak' and 'transcribe' are action-oriented, but this is reasonable given their distinct functions. The naming remains highly readable and predictable.
Tool Count5/5
With 5 tools, the set is well-scoped for a server focused on Whisper transcription and Telegram integration. Each tool earns its place by covering essential aspects: setup verification, model configuration, text-to-speech, audio transcription, and Telegram-specific handling. This count avoids bloat while providing complete functionality.
Completeness5/5
The tool surface offers complete coverage for the domain of audio transcription and synthesis with Telegram integration. It includes setup checks (check_backends), configuration (list_models), core operations (speak_text, transcribe_audio), and platform-specific handling (transcribe_telegram_voice). There are no obvious gaps; agents can perform end-to-end workflows without dead ends.
Maintenance
ActivityInactive
ResponsivenessNo issues