SMCP - Secure Model Context Protocol

🎬 Demo: Multi-Agent Business Intelligence in Action

Watch SMCP orchestrate multiple AI agents to generate real business intelligence reports! This demo shows CrewAI + SMCP working together to analyze e-commerce, SaaS, and IoT data using Qwen3 models, DuckDB, and secure multi-agent coordination.

🚀 Overview

SMCP (Secure Model Context Protocol) is a proof-of-concept that demonstrates how the Model Context Protocol (MCP) can be extended with security features and multi-agent coordination capabilities. This project explores potential improvements to MCP for scenarios requiring authentication, encryption, and agent-to-agent communication.

The Challenge

While MCP provides an excellent foundation for AI-model interactions, certain use cases may benefit from:

• 🔒 Security: Authentication and encryption capabilities
• 🤝 Multi-Agent Coordination: Agent-to-agent communication patterns
• 🔧 Flexible Configuration: Additional deployment options
• 📊 Extended Features: Audit trails and compliance considerations

What This Project Demonstrates

SMCP is a technical exploration that shows how MCP could be enhanced with:

• Multiple Security Approaches: From simple API keys to JWT and encryption experiments
• A2A Coordination Concepts: Multi-agent orchestration patterns
• Native Connectors: DuckDB and filesystem integration examples
• Security Experiments: JWT/OAuth2, ECDH key exchange, and AES encryption
• MCP Compatibility: Maintains compatibility while adding optional security layers

📚 Documentation

Architecture & Design

• Architecture Overview - Complete system architecture, data flows, and design patterns
• Demo Architectures - Detailed walkthroughs of each demo with step-by-step flows
• MCP vs SMCP Comparison - Comprehensive comparison between standard MCP and SMCP
• Use Cases - Real-world applications and implementation scenarios

Technical Guides

• AI SQL Generation Guide - Using LLMs for SQL query generation
• Connector Development Guide - Building custom connectors
• CrewAI Integration - Integrating CrewAI with SMCP

✨ Key Features

🔐 Security Mode Experiments

• Simple Mode: Basic API key authentication for testing
• Basic Mode: JWT + HTTPS/TLS exploration
• Encrypted Mode: ECDH + AES-256 encryption proof-of-concept
• Enterprise Mode: OAuth2 + audit trail concepts

🤖 Agent-to-Agent (A2A) System

• Multi-agent task orchestration
• Dynamic agent discovery
• Parallel and sequential workflows
• Load balancing and failover

🔌 Native Connectors

• DuckDB: High-performance analytical queries
• Filesystem: Secure local storage
• Extensible: Easy to add custom connectors

🏗️ Technical Features

• Configuration via TOML/YAML/ENV
• Logging and monitoring examples
• Connection pooling experiments
• Scaling pattern demonstrations

📦 Installation

Prerequisites

• Python 3.8+
• Ollama (for AI features)
• Docker (for MindsDB)
• Pixi package manager

Quick Start

1. Clone the repository:

git clone https://github.com/KellerKev/smcp.git
cd smcp

2. Install dependencies using pixi:

# Install pixi if you don't have it
curl -fsSL https://pixi.sh/install.sh | bash

# Install all dependencies
pixi install

3. Setup Ollama and AI Models:

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Start Ollama service
ollama serve &

# Pull required models (Qwen models recommended)
ollama pull qwen2.5-coder:7b-instruct-q4_K_M
ollama pull qwen3-coder:30b-a3b-q4_K_M

4. Initialize DuckDB with Sample Data:

# Generate sample data
pixi run python tools/generate_sample_data.py

# Create database and tables
pixi run python examples/duckdb_integration_example.py

5. Setup MindsDB (Optional - for ML features):

# Run MindsDB in Docker
docker run -d --name mindsdb_smcp \
  -p 47335:47334 \
  -p 47336:47335 \
  mindsdb/mindsdb

# Verify it's running
curl http://localhost:47335/

📚 Full Setup Guide: See SETUP_GUIDE.md for detailed instructions

🎯 Quick Demo

1. Basic Poem Generation

This demo shows multi-agent AI coordination:

# Start Ollama (in another terminal)
ollama serve

# Run the demo
python examples/basic/basic_poem_sample.py

What happens:

• TinyLLama generates an initial poem
• Mistral enhances it
• Result is securely stored locally
• Uses JWT authentication

2. DuckDB Analytics Demo

Shows database integration with AI analysis:

# Generate sample data
python tools/generate_sample_data.py

# Run analytics demo
python examples/duckdb_integration_example.py

Features demonstrated:

• SQL queries via SMCP connector
• AI-powered data analysis
• Business intelligence generation

3. Complete System Showcase

See all features in action:

python examples/showcase_complete_system.py

🏃‍♂️ Running Your First SMCP Server

Server Setup

1. Create configuration (optional):

python smcp_server_main.py --create-config

2. Start the server:

python smcp_server_main.py

Client Connection

from smcp_client import SMCPClient
from smcp_config import SMCPConfig

# Create configuration
config = SMCPConfig(
    mode="basic",
    server_url="ws://localhost:8765"
)

# Connect and use
client = SMCPClient(config)
await client.connect()

# Discover capabilities
capabilities = client.capabilities

# Invoke a tool
result = await client.invoke_tool("echo", {"message": "Hello SMCP!"})

# Disconnect
await client.disconnect()

🎭 Example Use Cases

Multi-Agent Report Generation

# Demonstrates CrewAI + SMCP for business intelligence
python examples/crewai_report_orchestration.py

Creates executive reports using:

• Data Analyst agent (queries DuckDB)
• Business Analyst agent (strategic insights)
• Report Writer agent (document generation)
• Quality Reviewer agent (validation)

Secure Enterprise Deployment

# Shows enterprise-grade security features
python examples/encrypted/encrypted_enterprise_sample.py

Features:

• ECDH key exchange
• AES-256 encryption
• Audit trails
• Compliance logging

📁 Project Structure

smcp/
├── smcp_*.py                 # Core SMCP modules
├── connectors/              # Native connector implementations
│   ├── smcp_duckdb_connector.py
│   └── smcp_filesystem_connector.py
├── examples/                # Demo applications
│   ├── basic/              # Basic security mode examples
│   ├── encrypted/          # Encrypted mode examples
│   └── *.py               # Integration examples
├── tools/                   # Utility scripts
│   ├── generate_sample_data.py
│   └── setup_dev_security.py
├── docs/                    # Documentation
└── sample_data/            # Sample datasets

🔧 Configuration

SMCP supports multiple configuration sources:

TOML Configuration

# smcp_config.toml
[core]
node_id = "production_node"
mode = "basic"  # or "encrypted", "enterprise"

[server]
host = "0.0.0.0"
port = 8765

[security]
jwt_secret = "your-secret-key-min-32-chars"
require_signature = true

Environment Variables

export SMCP_NODE_ID="production_node"
export SMCP_MODE="basic"
export SMCP_JWT_SECRET="your-secret-key"

Python Configuration

from smcp_config import SMCPConfig

config = SMCPConfig(
    mode="basic",
    node_id="my_node",
    jwt_secret="secret_key"
)

🛡️ Security Best Practices

1. Use appropriate security mode:

   • Development: simple mode
   • Production: basic mode with HTTPS
   • High security: encrypted mode
   • Compliance: enterprise mode

2. Secure your keys:

   • Use strong JWT secrets (32+ characters)
   • Rotate keys regularly
   • Never commit secrets to version control

3. Enable HTTPS for deployment:

   config.server_url = "wss://your-domain.com"  # WSS for secure WebSocket

🤝 MCP Compatibility

SMCP is 100% compatible with existing MCP tools and clients:

# Works with standard MCP clients
# SMCP server appears as enhanced MCP server
# All MCP tools continue to work

📊 Performance

• Message Processing: <10ms overhead for encryption
• A2A Coordination: <50ms for agent discovery
• Database Queries: Sub-second on 100K+ records
• Horizontal Scaling: Supports multiple nodes

🧪 Testing

Run the test suite:

# Compile all examples (syntax check)
find examples/ -name "*.py" -exec python3 -m py_compile {} \;

# Run basic test
python examples/basic/basic_poem_sample.py

📚 Documentation

• Connector Development Guide
• CrewAI Integration Guide
• AI SQL Generation Guide

🤲 Contributing

We welcome contributions! Please see our contributing guidelines.

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

• Built on top of the Model Context Protocol
• Uses Ollama for local AI models
• Integrates with CrewAI for orchestration
• Database features powered by DuckDB

🚦 Status

• ✅ Core SMCP: Proof-of-concept working
• ✅ Basic/Encrypted modes: Functional demonstrations
• ✅ A2A System: Working prototype
• ✅ DuckDB Connector: Example implementation
• ✅ CrewAI Integration: Working demo (requires CrewAI)
• 🚧 Enterprise Mode: Experimental

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Want to explore MCP security concepts? Start with the Quick Demo above!