
GenPark Conversational Barge-In Interruption Arbitrator
io.github.alphaparkincPublished by GitHub account alphaparkincv1.0.1Updated Oct 28, 2026
Arbitrate conversational interruptions with echo rules and playback truncation estimation
Overview
A local Python MCP skill that arbitrates barge-in interruptions in real-time voice agents, classifying interruptions, backchannels and echo.
- What you can do
- Arbitrate conversational interruptions using echo rules and playback truncation estimation
- Classify user barge-ins, backchannels and acoustic echo with context rollback
- Score turn-taking decisions with VAD energy profiling and jitter buffering
- Accept an audio frame, transcript or telemetry payload and return a decision with telemetry in a standardized JSON schema
- When to use it
- Worth adding when building or debugging full-duplex voice agents and streaming audio pipelines that need deterministic turn-taking decisions, such as deciding whether a user's speech is a real interruption or just a backchannel.
- Requirements
- Runs as a local stdio process started with Python 3.9+ (no external pip packages). The README configures it by pointing an MCP client at mcp_server.py; no accounts, API keys or environment variables are declared.
Installation
In SourceWeft
- Open GenPark Conversational Barge-In Interruption Arbitrator in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
genpark-conversational-barge-in-interruption-arbitrator-skill
[Python 3.9+] [License MIT] [MCP Compatible] [GenPark AI] [Zero Dependencies]
Production-Grade Real-Time Voice Agent & Conversational Audio Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
🌐 GenPark MCP Hub Showcase • 📦 Official Website • 📖 Documentation
📌 Overview & Capability
genpark-conversational-barge-in-interruption-arbitrator-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: Full-duplex conversational barge-in arbitrator classifying user interruptions, backchannels, and acoustic echo with context rollback.
⚡ Key Highlights & Value
- 🐍 Zero External
pipDependencies: 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
🚀 Quickstart & Usage
1. Direct Python Client Execution
2. Programmatic Integration
🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
📊 Technical Specifications
❓ 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.
Q3: How do I test this MCP server locally?
Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.
Source: README.md at commit 2fcee6a
Tools
0Version history
1- v1.0.1LatestOct 19, 2026
Score
40% of the applicable checks met
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- Reached in the last 30 days25 pts
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- Tools listed15 pts
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- DescriptionRequired15 pts
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- Documentation15 pts
- License10 pts
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