▶ THE FOUR PILLARS OF ADVANCED MCP
🔧 TOOLS
The heart of MCP servers
- Async streaming with
longRunningOperation - Structured metadata with
annotatedMessage - Fine-grained, focused functionality
- Intelligent LLM selection through rich schemas
📚 RESOURCES
Dynamic read-only context
- Auto-updating data feeds (
test://static/resource/42) - Subscription-based real-time updates
- Large dataset handling via URI references
- Efficient memory and bandwidth usage
💬 PROMPTS
Reusable conversation templates
- Dynamic argument injection (
resource_prompt) - Separation of prompt logic from content
- Template reusability across contexts
- External storage and version control
🤖 SAMPLING
Server-asks-model patterns
- Bidirectional AI workflows (
sampleLLM) - AI-assisted tool operations
- Always validate AI output before trusting
- Enable complex autonomous workflows
▶ ARCHITECTURAL PRINCIPLES
Everything Server = gold-standard reference implementation for all MCP patterns
┌──────────────────────────────────────────────────────────────────┐
│ MCP ARCHITECTURE PYRAMID │
├──────────────────────────────────────────────────────────────────┤
│ │
│ 🚀 PRODUCTION │
│ Security + Performance │
│ ┌─────────────────────────┐ │
│ │ Secure by Design │ │
│ │ • OAuth2 + Validation │ │
│ │ • Audit + Monitoring │ │
│ │ • Container + Limits │ │
│ └─────────────────────────┘ │
│ │ │
│ 🔧 COMPOSITION │
│ Multi-Server Architecture │
│ ┌─────────────────────────────────┐ │
│ │ Specialized Servers │ │
│ │ Git + FS + DB + Everything │ │
│ │ • Focused domains │ │
│ │ • Client orchestration │ │
│ │ • Unique namespaces │ │
│ └─────────────────────────────────┘ │
│ │ │
│ ⚡ CORE PRIMITIVES │
│ Tools + Resources + Prompts + Sampling │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Four Pillars │ │
│ │ • Async operations + Real-time data │ │
│ │ • Template systems + AI workflows │ │
│ │ • Structured metadata + Validation │ │
│ └─────────────────────────────────────────────────┘ │
│ │
└──────────────────────────────────────────────────────────────────┘
▶ PRODUCTION READINESS CHECKLIST
✅ MODULAR & TESTABLE
Build focused, single-purpose servers
✅ OBSERVABLE
Comprehensive logging, metrics, and tracing
✅ SECURE BY DESIGN
Authentication, validation, audit trails, sandboxing
✅ SCALABLE ARCHITECTURE
Async I/O, containerization, horizontal scaling
▶ FUTURE INNOVATION OPPORTUNITIES
🤖 AI-NATIVE PATTERNS
- Self-configuring servers based on usage patterns
- Predictive resource loading and caching
- Adaptive tool selection and optimization
- Autonomous workflow generation and tuning
🔗 ECOSYSTEM EXPANSION
- Industry-specific server libraries (finance, healthcare, etc.)
- Community-driven tool marketplaces
- Cross-platform compatibility layers
- Integration with emerging AI frameworks
📊 INTELLIGENCE LAYERS
- Advanced analytics on tool usage patterns
- Performance optimization recommendations
- Security threat detection and mitigation
- Business intelligence integration
▶ DEVELOPER PRODUCTIVITY GAINS
├── Faster Development │ Reusable components, clear patterns
├── Better Debugging │ Inspector tools, structured logging
├── Improved Security │ Built-in best practices, validation
├── Easier Scaling │ Container-ready, cloud-native design
└── Enhanced Collaboration │ Standardized interfaces, documentation
▶ BUILD MODULAR, TESTABLE, OBSERVABLE COMPONENTS
Remember: MCP servers provide tools, resources, prompts, and sampling as composable building blocks for AI-powered applications
Success Metrics:
- Developer Velocity: How quickly can teams build and deploy new AI capabilities?
- System Reliability: What's the uptime and error rate of production servers?
- Security Posture: How well are systems protected against threats?
- Business Value: What measurable improvements do AI workflows provide?