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:
- **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
- **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
- **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
- **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
- **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
- Agent submits request to server
- Server evaluates scores in sequence
- If any score exceeds its threshold Route to human
- If all scores pass Allow autonomous agent action
- 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