cyqlelabs/mcp-dual-cycle-reasoner

MCP Dual-Cycle Reasoner

A Model Context Protocol (MCP) server implementing the Dual-Cycle Metacognitive Reasoning Framework for autonomous agents.

Key Features

• 📊 Advanced Statistical Analysis - Entropy-based anomaly detection and time series analysis
• 🧠 Semantic Text Processing - NLP-powered belief revision and case similarity
• 🎯 Multi-Strategy Detection - Statistical, pattern-based, and hybrid loop detection
• 📈 Time Series Analysis - Trend detection and cyclical pattern recognition
• 🔧 Configurable Detection - Domain-specific thresholds and progress indicators
• 🚀 High-Performance Libraries - Built with simple-statistics, natural, and compromise

Architecture Overview

Based on the framework described in DUAL-CYCLE.MD, this implementation features:

• Cognitive Cycle (The "Doer"): Direct interaction with the environment
• Metacognitive Cycle (The "Thinker"): Monitors and controls the cognitive cycle

Installation

cd mcp-dual-cycle-reasoner
npm install
npm run build

Local Usage

{
  "mcpServers": {
    "dual-cycle-reasoner": {
      "command": "node",
      "args": ["/path/to/mcp-dual-cycle-reasoner/build/index.js"]
    }
  }
}

Using with Claude Desktop

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "dual-cycle-reasoner": {
      "command": "npx",
      "args": ["@cyqlelabs/mcp-dual-cycle-reasoner"]
    }
  }
}

Running the Server

npm start

Available Tools

Core Monitoring Tools

• start_monitoring: Start metacognitive monitoring of an agent's cognitive process.
• process_trace_update: Main monitoring function - process a cognitive trace update from the agent.
• stop_monitoring: Stop monitoring and get session summary.

Loop Detection Tools

• detect_loop: Detect if the agent is stuck in a loop using various strategies.
• configure_detection: Configure loop detection parameters and domain-specific progress indicators.

Failure Analysis Tools

• diagnose_failure: Diagnose the cause of a detected loop using abductive reasoning.
• revise_beliefs: Revise agent beliefs using AGM belief revision principles.

Recovery Tools

• generate_recovery_plan: Generate a recovery plan using case-based reasoning.

Experience Management

• store_experience: Store a case for future case-based reasoning.
• retrieve_similar_cases: Retrieve similar cases from the case base.

Schema Simplifications

The latest version features simplified schemas optimized for LLM usage.

Advanced Loop Detection Strategies

• Enhanced Action Trace Analysis: Entropy-based anomaly detection and autocorrelation analysis.
• Advanced State Invariance Tracking: MD5 hash-based state fingerprinting and statistical similarity measurement.

Recovery Patterns

The system implements five recovery patterns:

1. Strategic Retreat: Backtrack to known good state
2. Context Refresh: Clear state
3. Modality Switching: Switch from DOM to visual interaction
4. Information Foraging: Explore page structure systematically
5. Human Escalation: Request human intervention

Theoretical Foundation

This implementation combines cognitive science, AI research, and advanced computational methods.

Research Applications

This framework enables research in:

• Autonomous agent robustness: Preventing and recovering from failure states
• Metacognitive AI systems: Self-monitoring and self-regulation in AI agents

License

MIT License - see LICENSE file for details.

Contributing

Contributions welcome! Please read the contributing guidelines and ensure all tests pass.

Support

For issues and questions, please use the GitHub issue tracker.