mcp-human-loop
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The MCP Human Loop Server is designed to facilitate the collaborative efforts of humans and AI agents by determining when and how human intervention is necessary. It employs a sophisticated scoring system to make this determination, ensuring efficiency and transparency. The project aims to enhance decision-making processes in human-AI interaction.
MCP Human Loop Server
A Model Context Protocol server that manages human-agent collaboration through a sequential scoring system.
Core Concept
- Intelligent middleware for human intervention in AI agent operations
- Uses a scoring system to evaluate requests before requiring human input
Scoring System
- Complexity Score
- Permission Score
- Risk Score
- Emotional Intelligence Score
- Confidence Score
Flow Logic
- Agent submits request to server
- Server evaluates scores
- Route to human or autonomous action based on scores
- Log decisions
Benefits
- Efficiency
- Scalability
- Tunability
- Transparency
- Learning
Future Improvements
- Dynamic and real-time threshold adjustment
- Machine learning integration
- External system integration