Model Context Protocol (MCP)

Introduction to MCP

The Model Context Protocol (MCP) is a framework I've developed to extend AI capabilities through specialized, purpose-built services. It creates a standardized communication layer between language models and various tools, allowing AIs to perform tasks beyond their built-in capabilities while maintaining clear boundaries and contextual awareness.

At its core, MCP addresses a fundamental challenge in AI systems: how to combine the flexibility of general-purpose models with the precision of specialized tools, all while preserving essential context.

Core Architecture

MCP follows a client-server architecture:

┌─────────────┐       ┌─────────────┐       ┌─────────────┐
│             │       │             │       │             │
│  AI Model   │◄─────►│  MCP Client │◄─────►│ MCP Servers │
│             │       │             │       │             │
└─────────────┘       └─────────────┘       └─────────────┘
                                                   ▲
                                                   │
                                                   ▼
                                           ┌─────────────┐
                                           │ Specialized │
                                           │  Services   │
                                           └─────────────┘

Key Components

• AI Model: Large language models like Claude or open-source alternatives
• MCP Client: Interface that formats requests and responses between the model and servers
• MCP Servers: Specialized services that process specific types of requests
• Service Registry: Directory of available services and their capabilities

Protocol Design

MCP uses a structured message format that preserves context through all stages of processing:

Request Format

{
  "request_id": "unique-identifier",
  "model_context": {
    "conversation_history": [...],
    "current_query": "...",
    "relevant_information": {...}
  },
  "service_request": {
    "service_name": "service-identifier",
    "action": "requested-action",
    "parameters": {...},
    "constraints": {...}
  }
}

Response Format

{
  "request_id": "unique-identifier",
  "service_response": {
    "status": "success|error|partial",
    "content": {...},
    "metadata": {...}
  },
  "context_update": {
    "additions": {...},
    "modifications": {...}
  }
}

This structured approach ensures that:

1. Context is preserved throughout the processing chain
2. Service boundaries remain clear and explicit
3. Responses can be meaningfully integrated into ongoing conversations

Implemented Services

I've developed several specialized MCP services:

Memory Service

Provides long-term storage and retrieval of information with:

• Semantic search capabilities
• Temporal context awareness
• Structured metadata support

Filesystem Service

Enables safe, controlled access to local file systems with:

• Read/write operations
• Directory management
• File metadata access

Neo4j Integration

Connects AI models to graph databases for:

• Knowledge graph queries
• Relationship-based analysis
• Complex data structure manipulation

Homebridge Connection (Planned)

Will enable smart home control through:

• Device state queries
• Action execution
• Scene management

Implementation Examples

Claude Code Integration

MCP serves as the foundation for connecting Claude Code with specialized tooling:

# Configure MCP server for Claude Code
claudecode:
  command: claude
  args: [mcp, serve]
  env: {}

Digital Cortex

My Digital Cortex knowledge management system uses MCP to connect AI capabilities with specialized information processing:

# Example of connecting to the Memory service
memory_service = MCPClient.connect("memory")
response = memory_service.retrieve({
  "query": "nutshell theory principles",
  "context": conversation_context,
  "n_results": 5
})

Technical Benefits

The MCP framework provides several key technical advantages:

1. Modularity: Services can be developed and deployed independently
2. Extension: New capabilities can be added without modifying the core model
3. Specialization: Purpose-built tools can excel at specific tasks
4. Context Preservation: Essential information flows through the entire system
5. Security Boundaries: Clear separation between model and service capabilities

Practical Applications

MCP enables numerous practical applications:

• Personal Knowledge Management: Connected memory and retrieval systems
• Development Workflows: Code analysis, generation, and deployment
• Research Assistance: Literature review and information synthesis
• System Automation: Controlled interaction with local and networked systems

Getting Started with MCP

If you're interested in implementing MCP in your own projects:

1. Understand the Protocol: Review the message format and architecture
2. Start with Basic Services: Begin with simple file or memory services
3. Develop Clear Boundaries: Define explicit service capabilities and limitations
4. Ensure Context Flow: Make sure essential context traverses the entire system

Future Development

The MCP framework continues to evolve, with planned enhancements including:

• Service Discovery: Dynamic identification of available services
• Enhanced Security: More granular permission models
• Performance Optimization: Reduced latency and improved throughput
• Additional Services: Expanding the ecosystem of specialized tools

Conclusion

The Model Context Protocol represents a practical approach to extending AI capabilities while maintaining the principles outlined in Nutshell Theory. By creating clear boundaries, preserving context, and enabling specialized processing, MCP helps create AI systems that complement human capabilities rather than attempting to replace them.