mcp-human-loop
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A Model Context Protocol server that manages human-agent collaboration through a sequential scoring system.
The MCP Human Loop Server acts as an intelligent middleware that determines when human intervention is necessary in AI agent operations. It employs a sequential scoring system to evaluate multiple dimensions of a request, such as complexity, permission, risk, emotional intelligence, and confidence, before deciding if human input is required. This approach ensures that only truly necessary cases reach human operators, enhancing efficiency and scalability. The server's flow logic involves evaluating scores in sequence and routing requests to humans if any score exceeds its threshold. The system is designed to be tunable, transparent, and capable of learning from tracked outcomes, with future improvements planned for dynamic threshold adjustment and machine learning integration.
Features
- Sequential Scoring System: Evaluates requests through multiple dimensions to determine the need for human intervention.
- Flow Logic: Processes requests by evaluating scores and routing to humans if necessary, while logging decisions for improvement.
- Efficiency and Scalability: Ensures only necessary cases reach human operators and allows easy addition of new scoring dimensions.
- Tunability and Transparency: Thresholds can be adjusted based on experience, providing a clear decision path for interventions.
- Learning and Improvement: System improves through tracked outcomes and plans for future enhancements like machine learning integration.
