tts-audio-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| TTS_PYTHON_BIN | No | Python binary with librosa installed | .venv/bin/python3 |
| WHISPER_BINARY | No | Path to whisper.cpp binary | whisper-cli |
| WHISPER_MODEL_PATH | No | Path to Whisper model file | ~/Documents/_dev/tts-audio-mcp/models/ggml-large-v3-turbo.bin |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| transcribeA | Transcribe an audio file to text with word-level timestamps using Whisper |
| quality_scoreA | Analyze speech quality metrics of an audio file — pitch variation, energy, pacing, silence ratio. Detects robotic tone, monotone speech, and audio issues. |
| compare_ttsA | Compare TTS audio output against expected text — identifies mispronunciations, inserted/deleted words, and Word Error Rate (WER) |
| analyze_ttsA | Full TTS audio analysis — transcription, quality scores, pacing analysis, and optional mispronunciation detection. Returns a comprehensive report for debugging TTS issues. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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