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