Msp Fastmcp Services
MSP Enterprise AI Services built with FastMCP architecture
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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.
ποΈ 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
π 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 searchadd_document()- Add new knowledge articlesgenerate_answer()- AI-powered Q&Aupdate_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 callsexecute_call()- Perform automated callingget_call_analytics()- Calling performance metricscancel_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 sessionscontinue_conversation()- Process ongoing interactionsclassify_intent()- Understand customer needscreate_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 dataget_client_profitability_analysis()- Customer value metricsget_compliance_dashboard()- Regulatory status overviewgenerate_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 servicesexecute_cross_service_workflow()- Multi-service processesget_unified_analytics()- Comprehensive insights
π§ 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
- Clone the repository:
git clone https://github.com/mynewopportunities/msp-fastmcp-services.git
cd msp-fastmcp-services
- Set up environment variables:
cp .env.example .env
# Edit .env with your configuration
- Deploy with Docker Compose:
# Start all services
docker-compose up -d
# View logs
docker-compose logs -f
# Check service status
docker-compose ps
- Initialize the database:
# Run database migrations
docker-compose exec msp-orchestrator python init_db.py
π― 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
- Unified Protocol: All services speak the same language
- Enhanced AI Capabilities: Native support for AI tools and resources
- Better Observability: Built-in service introspection and monitoring
- Easier Integration: Standardized tool and resource interfaces
- Future-Proof: Designed for the AI-first era
π Service Architecture
βββββββββββββββββββββββββββββββββββββββββββββββββββ
β FastMCP Orchestrator β
β (Service Coordination Hub) β
βββββββββββββββββββββββ¬ββββββββββββββββββββββββββββ
β
βββββββββββββββββββΌββββββββββββββββββ
β β β
βΌ βΌ βΌ
βββββββββββ βββββββββββββββ βββββββββββββββ
β RAG β β Calling β β Chatbot β
β System β β Assistant β β Service β
βββββββββββ βββββββββββββββ βββββββββββββββ
β β β
βββββββββββββββββββΌββββββββββββββββββ
β
βΌ
βββββββββββββββββββ
β Analytics β
β Dashboard β
βββββββββββββββββββ
π 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']}")
π 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
π 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
π οΈ 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
π Deployment
Production Deployment
For production deployment:
# Start all services
docker-compose up -d
# Scale services as needed
docker-compose up -d --scale worker=3
π License
This project is licensed under the MIT License.
π€ Contributing
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Built with β€οΈ for the MSP community using FastMCP architecture
