MSP FastMCP Services

Enterprise-grade AI services built with FastMCP architecture for Managed Service Providers

A comprehensive suite of AI-powered services designed specifically for MSP operations, built using the FastMCP (Model Context Protocol) framework for maximum interoperability and performance.

🏗️ Architecture Overview

This system converts all traditional web services to FastMCP-based architecture, providing:

• Unified Protocol: All services communicate via the Model Context Protocol (MCP)
• Enhanced AI Integration: Native support for AI tools and resources
• Scalable Design: Microservices architecture with container orchestration
• Real-time Capabilities: WebSocket-based communication for instant responses

🚀 FastMCP Services

1. RAG Knowledge Management System (fastmcp_rag_system.py)

• Purpose: Intelligent knowledge base with semantic search
• Key Features:
  • Vector-based document embedding and retrieval
  • AI-powered answer generation
  • Multi-category knowledge organization
  • Real-time document indexing

FastMCP Tools:

• search_knowledge_base() - Semantic document search
• add_document() - Add new knowledge articles
• generate_answer() - AI-powered Q&A
• update_document() - Modify existing content

2. AI Calling Assistant (fastmcp_calling_assistant.py)

• Purpose: Automated voice calling with AI conversation management
• Key Features:
  • Intelligent call scheduling and execution
  • Real-time speech processing and response
  • Multi-type call support (sales, support, reminders)
  • Call outcome analysis and follow-up automation

FastMCP Tools:

• schedule_call() - Schedule AI-powered calls
• execute_call() - Perform automated calling
• get_call_analytics() - Calling performance metrics
• cancel_call() - Cancel scheduled calls

3. Intelligent Chatbot (fastmcp_intelligent_chatbot.py)

• Purpose: Multi-channel conversational AI for customer engagement
• Key Features:
  • Advanced intent classification and entity extraction
  • Context-aware conversation management
  • Seamless escalation to human agents
  • Integration with support ticket system

FastMCP Tools:

• start_conversation() - Initialize new chat sessions
• continue_conversation() - Process ongoing interactions
• classify_intent() - Understand customer needs
• create_support_ticket() - Generate tickets from chats

4. Analytics Dashboard (fastmcp_analytics_dashboard.py)

• Purpose: Comprehensive business intelligence and reporting
• Key Features:
  • Real-time revenue and profitability analysis
  • Client satisfaction and retention metrics
  • Compliance dashboard with automated reporting
  • Predictive analytics for business optimization

FastMCP Tools:

• get_revenue_analytics() - Financial performance data
• get_client_profitability_analysis() - Customer value metrics
• get_compliance_dashboard() - Regulatory status overview
• generate_chart_data() - Visualization data export

5. Service Orchestrator (msp_service_orchestrator.py)

• Purpose: Central coordination hub for all FastMCP services
• Key Features:
  • Cross-service workflow automation
  • Unified service monitoring and health checks
  • Intelligent load balancing and failover
  • Consolidated analytics and reporting

FastMCP Tools:

• get_service_status() - Monitor all services
• execute_cross_service_workflow() - Multi-service processes
• get_unified_analytics() - Comprehensive insights

🔧 Installation & Deployment

Prerequisites

• Docker & Docker Compose
• Python 3.11+
• PostgreSQL 15+ with pgvector extension
• Redis 7+
• OpenAI API key
• Twilio account (for calling features)

Quick Start

1. Clone the repository:

git clone https://github.com/mynewopportunities/msp-fastmcp-services.git
cd msp-fastmcp-services

2. Set up environment variables:

cp .env.example .env
# Edit .env with your configuration

3. Deploy with Docker Compose:

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

# Check service status
docker-compose ps

4. Initialize the database:

# Run database migrations
docker-compose exec msp-orchestrator python init_db.py

🎯 Key FastMCP Advantages

Traditional vs FastMCP Architecture

Aspect	Traditional	FastMCP
Communication	REST/HTTP APIs	Model Context Protocol
AI Integration	Custom implementations	Native MCP tools/resources
Interoperability	Service-specific	Standardized protocol
Tool Discovery	Manual documentation	Automatic introspection
Context Sharing	Limited	Rich context passing
Real-time	Polling/WebHooks	Native streaming

Benefits of FastMCP

1. Unified Protocol: All services speak the same language
2. Enhanced AI Capabilities: Native support for AI tools and resources
3. Better Observability: Built-in service introspection and monitoring
4. Easier Integration: Standardized tool and resource interfaces
5. Future-Proof: Designed for the AI-first era

📊 Service Architecture

┌─────────────────────────────────────────────────┐
│              FastMCP Orchestrator               │
│         (Service Coordination Hub)              │
└─────────────────────┬───────────────────────────┘
                      │
    ┌─────────────────┼─────────────────┐
    │                 │                 │
    ▼                 ▼                 ▼
┌─────────┐   ┌─────────────┐   ┌─────────────┐
│   RAG   │   │   Calling   │   │   Chatbot   │
│ System  │   │ Assistant   │   │   Service   │
└─────────┘   └─────────────┘   └─────────────┘
    │                 │                 │
    └─────────────────┼─────────────────┘
                      │
                      ▼
            ┌─────────────────┐
            │    Analytics    │
            │    Dashboard    │
            └─────────────────┘

🚀 Usage Examples

Starting a Conversation

from mcp import Client

# Connect to chatbot service
client = Client("ws://localhost:8003/mcp")

# Start conversation
result = await client.call_tool("start_conversation", {
    "user_message": "I need help with my server backup",
    "channel": "web",
    "user_id": "customer_123"
})

print(f"Session ID: {result['session_id']}")
print(f"Response: {result['response']}")

Searching Knowledge Base

# Connect to RAG service
rag_client = Client("ws://localhost:8001/mcp")

# Search for information
results = await rag_client.call_tool("search_knowledge_base", {
    "query": "server backup best practices",
    "category": "technical",
    "limit": 5
})

for doc in results:
    print(f"Title: {doc['title']}")
    print(f"Relevance: {doc['similarity_score']}")

Scheduling AI Calls

# Connect to calling service
call_client = Client("ws://localhost:8002/mcp")

# Schedule a call
call_result = await call_client.call_tool("schedule_call", {
    "contact_id": 123,
    "call_type": "customer_support",
    "scheduled_time": "2024-01-15T14:30:00Z",
    "notes": "Follow up on backup issue"
})

print(f"Call ID: {call_result['call_id']}")

📈 Monitoring & Observability

Service Health Monitoring

• Prometheus: Metrics collection and alerting
• Grafana: Real-time dashboards and visualization
• ELK Stack: Centralized logging and log analysis
• Health Checks: Built-in service health endpoints

Accessing Dashboards

• Grafana: http://localhost:3000 (admin/password)
• Prometheus: http://localhost:9090
• Kibana: http://localhost:5601
• RabbitMQ Management: http://localhost:15672

🔒 Security Features

• Encryption: All sensitive data encrypted at rest and in transit
• Authentication: Multi-factor authentication support
• Authorization: Role-based access control (RBAC)
• Audit Logging: Comprehensive audit trails for all operations
• Data Privacy: GDPR/CCPA compliant data handling

🛠️ Development

Development Setup

# Create development environment
python -m venv fastmcp-dev
source fastmcp-dev/bin/activate  # On Windows: fastmcp-dev\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run services locally
python msp_service_orchestrator.py

Code Quality

• Black: Code formatting
• isort: Import sorting
• flake8: Linting
• mypy: Type checking
• pre-commit: Git hooks for code quality

🚀 Deployment

Production Deployment

For production deployment:

# Start all services
docker-compose up -d

# Scale services as needed
docker-compose up -d --scale worker=3

📄 License

This project is licensed under the MIT License.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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Built with ❤️ for the MSP community using FastMCP architecture