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Duration 14 hours
Course Outline
MCP Fundamentals and Enterprise Applications
- Overview of the Model Context Protocol and its role in enterprise AI integration
- Interaction dynamics between MCP servers, clients, models, tools, and backend systems
- Typical use cases, advantages, and limitations in collaborative team environments
- Critical design factors for successful production adoption
Architecting MCP Servers and Clients
- Establishing capabilities, contracts, and distinct responsibilities between server and client modules
- Organizing tools, resources, and prompts to ensure maintainability and reusability
- Implementing validation, standardized outputs, and informative error responses
- Crafting workflows that facilitate team ownership and long-term support
Ensuring Reliability and Security in Production
- Managing failures, invalid inputs, and downstream service disruptions
- Employing timeouts, retries, fallback mechanisms, and secure processing techniques
- Integrating authentication, authorization, and secure secret management
- Enabling audit trails and controlled access to enterprise tools and data
Deployment, Observability, and Operations
- Packaging and deploying MCP services across local, containerized, or cloud environments
- Managing configuration differences and release cycles across environments
- Setting up logs, metrics, health checks, and alerts for runtime visibility
- Diagnosing common operational challenges across clients and backend integrations
Testing, Versioning, and Change Governance
- Developing unit, integration, and contract tests for MCP workflows
- Managing interface evolution and maintaining compatibility over time
- Validating releases prior to rollout to minimize upgrade risks
- Utilizing practical readiness assessments for continuous support and maintenance
Practical Implementation Workshop
- Developing a basic enterprise-ready MCP server and client workflow
- Applying practices for validation, resilience, security, and observability
- Evaluating a production readiness checklist
- Formulating next steps for adoption across internal teams and platforms
Requirements
- Understanding of APIs, JSON, and fundamental client-server integration principles
- Proficiency with command-line utilities, Git, and basic application deployment processes
- Foundational programming knowledge in Python, JavaScript, or a comparable language
Target Audience
- Software engineers developing MCP-compatible applications and integrations
- Solution architects and technical leads overseeing enterprise AI adoption
- Platform, DevOps, and engineering teams maintaining production MCP services
