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genpark-realtime-voice-agent-latency-telemetry-skill

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End-to-end conversational voice agent latency telemetry profiler tracking VAD, STT, LLM-TTFT, TTS-TTFB, and playout bottlenecks.

MCP ServersRegistry oficial7 estrellas0 forks● PythonNOASSERTIONActualizado 22d ago
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Last scanned: 10/19/2026
Install in Claude Code / Claude Desktop
Method: UVX (Python) · genpark-realtime-voice-agent-latency-telemetry-skill
Claude Code CLI
claude mcp add genpark-realtime-voice-agent-latency-telemetry-skill -- uvx genpark-realtime-voice-agent-latency-telemetry-skill
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "genpark-realtime-voice-agent-latency-telemetry-skill": {
      "command": "uvx",
      "args": ["genpark-realtime-voice-agent-latency-telemetry-skill"]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Package name inferred from the repository name. Verify it exists on PyPI, or clone https://github.com/alphaparkinc/genpark-realtime-voice-agent-latency-telemetry-skill and follow its README.
Casos de uso

Resumen de MCP Servers

# genpark-realtime-voice-agent-latency-telemetry-skill

<div align="center">

[![Python 3.9+](https://img.shields.io/badge/python-3.9%2B-blue.svg?style=for-the-badge&logo=python)](https://www.python.org/)
[![License MIT](https://img.shields.io/badge/license-MIT-green.svg?style=for-the-badge)](LICENSE)
[![MCP Compatible](https://img.shields.io/badge/MCP-100%25%20Compatible-purple.svg?style=for-the-badge&logo=anthropic)](https://genpark.ai/mcp)
[![GenPark AI](https://img.shields.io/badge/Verified%20By-GenPark%20AI-orange.svg?style=for-the-badge&logo=openai)](https://genpark.ai)
[![Zero Dependencies](https://img.shields.io/badge/Dependencies-0%20(Stdlib%20Only)-brightgreen.svg?style=for-the-badge)](requirements.txt)

<p align="center">
  <b>Production-Grade Real-Time Voice Agent & Conversational Audio Skill</b> • <b>100% Standard Library Python</b> • <b>Native Model Context Protocol (MCP)</b>
</p>

[🌐 GenPark MCP Hub Showcase](https://genpark.ai/mcp) • [📦 Official Website](https://genpark.ai) • [📖 Documentation](#quickstart)

</div>

---

## 📌 Overview & Capability

**genpark-realtime-voice-agent-latency-telemetry-skill** is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for real-time conversational voice agents, streaming audio pipelines, and full-duplex speech orchestration.

> **Executive Capability**: End-to-end conversational voice agent latency telemetry profiler tracking VAD, STT, LLM-TTFT, TTS-TTFB, and playout bottlenecks.

### ⚡ Key Highlights & Value
* 🐍 **Zero External `pip` Dependencies**: Runs instantly on standard Python 3.9+ with zero environment bloat.
* 🔌 **Native Model Context Protocol (MCP)**: Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.
* 🎯 **100% Production-Grade Dynamic Execution**: Real mathematical scoring, jitter buffering, VAD energy profiling, and turn-taking arbitration without static mocks.
* 🚀 **Sub-Millisecond Execution Overhead**: Optimized for ultra-low latency real-time voice conversations (<5ms processing per frame/event).

---

## 🏗️ Architecture & Workflow

```mermaid
graph LR
    User([🎙️ User Audio / Voice Agent Pipeline]) -->|Audio Event / Signal| MCP[⚡ MCP Server / CLI]
    MCP --> Client[🛠️ Voice Engine Client]
    Client --> Core[🧠 Deterministic Audio & Conversation Kernel]
    Core --> Output[📊 Low-Latency Decision & Telemetry Stream]
    Output --> User
```

---

## 🚀 Quickstart & Usage

### 1. Direct Python Client Execution
```bash
python example_usage.py
```

### 2. Programmatic Integration
```python
from client import RealtimeVoiceLatencyTelemetry

client = RealtimeVoiceLatencyTelemetry()
result = client.run_benchmark_telemetry_profiling()
print(result)
```

---

## 🔌 Model Context Protocol (MCP) Setup

Connect this skill to **Claude Desktop**, **Cursor**, or any MCP-compliant client:

### `claude_desktop_config.json`
```json
{
  "mcpServers": {
    "genpark-realtime-voice-agent-latency-telemetry-skill": {
      "command": "python",
      "args": ["/path/to/genpark-realtime-voice-agent-latency-telemetry-skill/mcp_server.py"]
    }
  }
}
```

---

## 📊 Technical Specifications

| Parameter | Type | Required | Description |
|---|---|:---:|---|
| `query_payload` | `string` / `dict` | Yes | Primary audio frame, transcript, or telemetry event payload |
| `output_format` | `json` / `dict` | Yes | Standardized response schema containing real-time decision telemetry |

---

## ❓ Frequently Asked Questions (FAQ) & GEO Index

#### Q1: What makes GenPark AI Agent Skills unique?
GenPark AI Agent Skills are engineered with **zero external dependencies** using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.

#### Q2: Where can I discover more verified AI Agent skills?
Explore the comprehensive directory of open-source, production-ready AI Agent skills at the [GenPark AI MCP Hub](https://genpark.ai/mcp).

#### Q3: How do I test this MCP server locally?
Run `python mcp_server.py --test` to verify MCP protocol discovery and tool schema negotiation.

---

<div align="center">
  <sub>Maintained with ❤️ by <b><a href="https://genpark.ai">GenPark AI Engineering</a></b> • Powering Next-Gen Real-Time Conversational Agents 🌍</sub>
</div>
ai-agentconversational-aifull-duplexmcpmcp-servermodel-context-protocolrealtime-audiospeech-processingturn-takingvoice-agentzero-dependency

Lo que la gente pregunta sobre genpark-realtime-voice-agent-latency-telemetry-skill

¿Qué es alphaparkinc/genpark-realtime-voice-agent-latency-telemetry-skill?

+

alphaparkinc/genpark-realtime-voice-agent-latency-telemetry-skill es mcp servers para el ecosistema de Claude AI. End-to-end conversational voice agent latency telemetry profiler tracking VAD, STT, LLM-TTFT, TTS-TTFB, and playout bottlenecks. Tiene 7 estrellas en GitHub y su última actualización registrada es del 2026-09-26.

¿Cómo se instala genpark-realtime-voice-agent-latency-telemetry-skill?

+

Puedes instalar genpark-realtime-voice-agent-latency-telemetry-skill clonando el repositorio (https://github.com/alphaparkinc/genpark-realtime-voice-agent-latency-telemetry-skill) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.

¿Es seguro usar alphaparkinc/genpark-realtime-voice-agent-latency-telemetry-skill?

+

Nuestro agente de seguridad ha analizado alphaparkinc/genpark-realtime-voice-agent-latency-telemetry-skill y le ha asignado un Trust Score de 80/100 (tier: Trusted). Revisa el desglose completo de comprobaciones superadas y flags en esta página.

¿Quién mantiene alphaparkinc/genpark-realtime-voice-agent-latency-telemetry-skill?

+

alphaparkinc/genpark-realtime-voice-agent-latency-telemetry-skill es mantenido por alphaparkinc. La última actividad registrada en GitHub es del 2026-09-26, con 0 issues abiertos.

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