MCP Human Loop Server

MCP Human Loop Server

A Model Context Protocol server that manages human-agent collaboration through a sequential scoring system.

Core Concept

This server acts as an intelligent middleware that determines when human intervention is necessary in AI agent operations. Instead of treating human involvement as a binary decision, it uses a sequential scoring system that evaluates multiple dimensions of a request before deciding if human input is required.

Scoring System

The server evaluates requests through a series of scoring gates. Each gate represents a specific dimension that might require human intervention. A request only proceeds to human review if it triggers threshold values in any of these dimensions:

1. Complexity Score
   • Evaluates if the task is too complex for autonomous agent handling
   • Considers factors like number of steps, dependencies, and decision branches
   • Example: Multi-step tasks with uncertain outcomes score higher
2. Permission Score
   • Assesses if the requested action requires human authorization
   • Based on predefined permission levels and action types
   • Example: Financial transactions above certain amounts require human approval
3. Risk Score
   • Measures potential impact and reversibility of actions
   • Considers both direct and indirect consequences
   • Example: Actions affecting multiple systems or user data score higher
4. Emotional Intelligence Score
   • Determines if the task requires human emotional understanding
   • Evaluates context and user state
   • Example: User frustration or sensitive situations trigger human involvement
5. Confidence Score
   • Reflects the agent's certainty about its proposed action
   • Lower confidence triggers human review
   • Example: Edge cases or unusual patterns lower confidence

Flow Logic

1. Agent submits request to server
2. Server evaluates scores in sequence
3. If any score exceeds its threshold → Route to human
4. If all scores pass → Allow autonomous agent action
5. Track and log all decisions for system improvement

Benefits

• Efficiency: Only truly necessary cases reach human operators
• Scalability: Easy to add new scoring dimensions
• Tunability: Thresholds can be adjusted based on experience
• Transparency: Clear decision path for each human intervention
• Learning: System improves through tracked outcomes

Future Improvements

• Dynamic threshold adjustment based on outcome tracking
• Machine learning integration for score calculation
• Real-time threshold adjustment based on operator load
• Integration with external risk assessment systems

Installation

[Installation instructions to be added]

Usage

[Usage examples to be added]

Contributing

[Contribution guidelines to be added]

ToDo

Conversational Quality Monitoring

• Assess the depth and constructiveness of dialogue
• Detect repetitive or circular conversations
• Identify when a conversation lacks meaningful progress

Cognitive Load Management

• Evaluate the complexity of tasks or discussions
• Warn when the cognitive demands exceed typical processing capabilities
• Suggest breaking down complex topics or taking breaks

Learning and Skill Development Tracking

• Monitor the educational potential of conversations
• Identify when a discussion moves beyond or falls short of a learner's current skill level
• Recommend supplementary resources or adjust explanation complexity

Emotional Intelligence and Sentiment Analysis

• Detect potential emotional escalation in conversations
• Identify when a discussion becomes overly emotional or unproductive
• Suggest de-escalation strategies or communication adjustments

Compliance and Ethical Boundary Monitoring

• Proactively identify conversations approaching ethical boundaries
• Detect potential violations of predefined communication guidelines
• Provide early warnings about sensitive or potentially inappropriate content

Multi-Agent Coordination

• In scenarios with multiple AI agents or models
• Determine when to escalate or hand off tasks between different AI capabilities
• Optimize task allocation based on specialized skills

Resource Allocation and Performance Optimization

• Assess computational complexity of ongoing tasks
• Predict and manage computational resource requirements
• Optimize system performance by intelligently routing or prioritizing tasks

Cross-Disciplinary Knowledge Integration

• Detect when a conversation requires expertise from multiple domains
• Identify knowledge gaps or areas needing interdisciplinary insights
• Suggest bringing in additional contextual information or expert perspectives

Creativity and Innovation Detection

• Recognize when a conversation is generating novel ideas
• Identify potential breakthrough thinking or unique problem-solving approaches
• Encourage and highlight innovative thought patterns

Meta-Cognitive Analysis

• Analyze the reasoning and thought processes within a conversation
• Detect logical fallacies or cognitive biases
• Provide insights into the quality of reasoning and argumentation

Contextual Relevance in Research and Information Gathering

• Evaluate the relevance and comprehensiveness of information collection
• Detect when research is becoming too narrow or too broad
• Suggest alternative approaches or additional sources

Personalization and Adaptive Communication

• Learn and adapt communication styles based on interaction patterns
• Detect user preferences and communication effectiveness
• Dynamically adjust interaction strategies