Genpark Voice Turn Taking Endpoint Detector Skill MCP Server

genpark-voice-vad

Energy and transcript heuristics for voice turn endpoint detection.

This is a heuristic endpoint detector over precomputed dB energies and a transcript. It does not decode audio, transcribe speech, or run a trained VAD model. Confidence values are heuristic scores, not calibrated probabilities.

Install from the GitHub release

Python 3.9 or newer. The library and stdio MCP server have no runtime dependencies.

python -m pip install https://github.com/Alpha-Park/genpark-voice-turn-taking-endpoint-detector-skill/releases/download/v1.0.1/genpark_voice_vad-1.0.1-py3-none-any.whl

PyPI publication is pending account setup. The intended PyPI project is genpark-voice-vad; do not assume pip install genpark-voice-vad is available until the project is published.

Python usage

from genpark_voice_vad import VoiceTurnTakingEndpointDetector
client = VoiceTurnTakingEndpointDetector()
print(client.run_benchmark_turn_detection())

MCP stdio configuration

After installing the wheel, configure your MCP client with the installed command:

{
  "mcpServers": {
    "genpark-voice-vad": {
      "command": "genpark-voice-vad",
      "args": []
    }
  }
}

If the command is not on PATH, use its absolute path or python -m genpark_voice_vad with the same interpreter where you installed the wheel. The GitHub release also contains a .mcpb bundle for clients supporting desktop extensions. That bundle requires a Python 3.9+ interpreter on PATH; it bundles the server source.

Available tools: analyze_turn_status, calibrate_acoustic_thresholds, predict_semantic_closure, run_benchmark_turn_detection. tools/list returns required arguments and JSON schemas. Each MCP process holds its own state. Benchmark tools use isolated instances.

Development

python -m unittest discover -s tests
python -m pip install mcp
python tests/check_mcp.py
python -m pip install build twine
python -m build
python -m twine check dist/*

python mcp_server.py --test runs the deterministic example; it is not a protocol conformance test. The MCP client check exercises initialize, tools/list, tools/call and ping over stdio.

Distribution

GitHub source and release artifacts are the primary distribution until PyPI is configured. Registry submissions are tracked separately; a manifest is not proof of registry acceptance. See PUBLISHING.md for the repeatable PyPI workflow.

MIT license. Maintained by GenPark.