Advanced-AI-MCP

NoobDataScience/Advanced-AI-MCP

3.1

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The Advanced AI Memory & Reasoning MCP Server is a sophisticated system designed to enhance Claude with advanced memory management and reasoning capabilities.

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🧠 Advanced AI Memory & Reasoning MCP Server

A sophisticated Model Context Protocol (MCP) server that enhances Claude with genius-level memory management and Tree-of-Thought reasoning capabilities, inspired by cutting-edge AI research.

✨ Key Features

🏗️ Hierarchical Memory System

  • Core Memory: 15 slots for permanent, always-accessible information
  • Working Memory: 75 slots for current session context
  • Archival Memory: 2000 slots for long-term storage with intelligent forgetting curves
  • Smart Management: Automatic cleanup based on importance and access patterns

🌳 Tree-of-Thought Reasoning

  • Multi-Path Analysis: Analytical, creative, critical, and pragmatic approaches
  • Configurable Depth: 1-5 levels of reasoning expansion
  • Confidence Scoring: Each reasoning step includes confidence assessment
  • Path Optimization: Automatically selects highest-confidence reasoning paths

📊 Advanced Analytics

  • Conversation Context Analysis: Patterns, topics, and memory distribution
  • Cognitive Load Assessment: Task complexity evaluation with approach recommendations
  • Memory Optimization: Performance monitoring and automatic tuning
  • Export Capabilities: Backup and analysis of conversation memories

🚀 Installation

1. Run Setup

cd /Volumes/IOSStore/MCP/advanced-ai-mcp
chmod +x setup.sh
./setup.sh

2. Configure Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "advanced-ai-server": {
      "command": "python",
      "args": ["/Volumes/IOSStore/MCP/advanced-ai-mcp/advanced_mcp_server.py"],
      "env": {}
    }
  }
}

3. Restart Claude Desktop

🛠️ Available Tools

Memory Management

store_memory(content, memory_type="working", importance=0.5, tags="")

Store information in hierarchical memory system

# Examples:
store_memory("User prefers detailed technical explanations", "core", 0.9, "preference,communication")
store_memory("Working on advanced MCP server project", "working", 0.7, "project,development")
store_memory("Research notes on Tree-of-Thought reasoning", "archival", 0.6, "research,reasoning")
retrieve_memories(query="", memory_type="", limit=10)

Search and retrieve stored memories

# Examples:
retrieve_memories("user prefer", limit=5)
retrieve_memories(memory_type="core")
retrieve_memories("project", "working", 3)

Advanced Reasoning

advanced_reasoning(problem, max_depth=3)

Multi-path Tree-of-Thought analysis

# Examples:
advanced_reasoning("How should we improve team productivity while maintaining code quality?", 4)
advanced_reasoning("What are the key challenges in implementing AGI safely?", 5)
cognitive_load_assessment(task_description)

Assess task complexity and recommend optimal approach

# Examples:
cognitive_load_assessment("Design a distributed system architecture for real-time data processing")
cognitive_load_assessment("Write a simple function to calculate fibonacci numbers")

Analysis Tools

analyze_conversation_context()

Analyze current conversation using stored memories

# No parameters - analyzes recent conversation patterns, topics, and memory distribution
memory_optimization()

Optimize memory system performance

# No parameters - analyzes usage patterns and applies optimization
export_conversation_memory()

Export memories for backup or analysis

# No parameters - creates exportable summary of conversation memories

💡 Usage Examples

Basic Workflow

# 1. Store important context
store_memory("This is a technical discussion about AI reasoning systems", "working", 0.8, "context,ai,reasoning")

# 2. Analyze complex problems
advanced_reasoning("How can we create more efficient AI reasoning systems?", 4)

# 3. Monitor conversation
analyze_conversation_context()

# 4. Optimize performance
memory_optimization()

Project Context Management

# Store project requirements
store_memory("Building MCP server with memory and reasoning capabilities", "core", 0.9, "project,requirements")

# Store technical decisions  
store_memory("Using SQLite for persistence, Tree-of-Thought for reasoning", "working", 0.7, "technical,decisions")

# Analyze project complexity
cognitive_load_assessment("Implement advanced AI reasoning with persistent memory management")

Research and Analysis

# Store research findings
store_memory("OpenAI o3 achieves 87.5% on ARC-AGI benchmark using test-time compute scaling", "archival", 0.8, "research,benchmarks")

# Deep analysis
advanced_reasoning("What are the implications of test-time compute scaling for AGI development?", 5)

# Export for further analysis
export_conversation_memory()

🔧 System Architecture

Memory Hierarchy

Core Memory (15 slots)
├── Permanent user preferences
├── Key project information  
└── Critical context that should never be forgotten

Working Memory (75 slots)
├── Current session context
├── Active project details
└── Recently accessed information

Archival Memory (2000 slots)
├── Historical conversation data
├── Research notes and findings
└── Long-term knowledge with forgetting curves

Reasoning System

Tree-of-Thought Reasoning
├── Analytical: Logical structure analysis
├── Creative: Alternative perspectives and analogies
├── Critical: Assumption questioning and bias identification
└── Pragmatic: Practical implementation considerations

Database Schema

-- Memory storage with metadata
memories (id, content, memory_type, importance, created_at, last_accessed, access_count, tags, context)

-- Complete reasoning chains
reasoning_chains (chain_id, step_id, content, confidence, reasoning_type, parent_step, children, created_at)

⚡ Performance Features

Intelligent Memory Management

  • Importance-based retention: High-importance memories persist longer
  • Access-pattern optimization: Frequently accessed memories stay available
  • Forgetting curves: Realistic memory degradation over time
  • Automatic cleanup: Maintains optimal performance

Reasoning Optimization

  • Confidence-based path selection: Follows highest-confidence reasoning paths
  • Multi-strategy expansion: Uses 4 different cognitive approaches
  • Depth-aware scaling: Adjusts confidence based on reasoning depth
  • Chain persistence: Complete reasoning processes stored for analysis

Real-time Analytics

  • Memory usage statistics: Track memory distribution and patterns
  • Reasoning performance metrics: Analyze reasoning chain effectiveness
  • Conversation insights: Identify key topics and discussion patterns
  • System optimization: Automated performance tuning

🔍 Troubleshooting

Common Issues

Server not appearing in Claude:

  • Check JSON syntax in config file
  • Verify file paths are absolute and correct
  • Restart Claude Desktop completely

Import errors:

cd /Volumes/IOSStore/MCP/advanced-ai-mcp
source venv/bin/activate
pip install mcp fastmcp

Database errors:

# Reset database if corrupted
rm advanced_memory.db

Permission errors:

chmod +x advanced_mcp_server.py
chmod +x setup.sh

Debug Mode

Edit the server file to enable detailed logging:

logging.basicConfig(level=logging.DEBUG)

🛡️ Security & Privacy

  • Local storage only: All data stored in local SQLite database
  • No external communication: Server operates entirely offline
  • Encrypted storage option: Database can be encrypted if needed
  • Access control: Memory system respects importance and access patterns

🔮 Advanced Configuration

Memory Limits (edit in server file)

self.memory_limits = {
    "core": 20,      # Increase for more permanent memory
    "working": 100,  # Increase for larger working memory
    "archival": 3000 # Increase for more long-term storage
}

Reasoning Depth

# Allow deeper reasoning (up to 7 levels)
max_depth = max(1, min(7, max_depth))

Database Location

# Custom database path
memory_system = AdvancedMemorySystem(db_path="/custom/path/memory.db")

📊 Performance Monitoring

The server includes comprehensive monitoring:

  • Memory usage tracking and optimization
  • Reasoning chain analysis and metrics
  • Conversation pattern recognition
  • Automatic performance tuning

🤝 Integration Tips

Best Practices

  1. Start with context: Store project/conversation context in core memory
  2. Use appropriate types: Core for permanent, working for session, archival for history
  3. Tag consistently: Use consistent tagging for better retrieval
  4. Assess complexity: Use cognitive load assessment for complex tasks
  5. Monitor regularly: Run optimization and analysis tools periodically

Workflow Recommendations

  1. Project Setup: Store requirements and context in core memory
  2. Active Work: Use working memory for current session information
  3. Complex Analysis: Apply advanced reasoning for difficult problems
  4. Regular Maintenance: Optimize memory system and analyze patterns
  5. Knowledge Preservation: Export important memories for backup

📈 Real-World Applications

  • Software Development: Project context, technical decisions, code review insights
  • Research: Literature notes, hypothesis tracking, analysis patterns
  • Strategy Planning: Decision frameworks, option analysis, constraint identification
  • Learning: Concept connections, skill development tracking, knowledge synthesis
  • Problem Solving: Multi-perspective analysis, solution evaluation, implementation planning

Your Advanced AI MCP Server is ready to provide Claude with sophisticated memory and reasoning capabilities! 🧠✨