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Duration 14 hours
Course Outline
MCP Foundations and Enterprise Use Cases
- Understanding the Model Context Protocol and its role in enterprise AI integration
- How MCP servers and clients interact with models, tools, and backend systems
- Common use cases, benefits, and constraints in team-based environments
- Key design considerations for production adoption
Designing MCP Servers and Clients
- Defining capabilities, contracts, and clear responsibilities between server and client components
- Structuring tools, resources, and prompts for maintainability and reuse
- Applying validation, consistent outputs, and meaningful error responses
- Designing workflows that support team ownership and long-term maintenance
Reliability and Security in Production
- Handling failures, invalid requests, and downstream service issues
- Implementing timeouts, retries, fallback strategies, and safe processing patterns
- Applying authentication, authorization, and secure secret handling basics
- Ensuring auditability and controlled access to enterprise tools and data
Deployment, Observability, and Operations
- Packaging and deploying MCP services in local, containerized, or cloud environments
- Managing configuration, environment differences, and release workflows
- Implementing logs, metrics, health checks, and alerting for runtime visibility
- Troubleshooting common operational issues across clients and backend integrations
Testing, Versioning, and Change Management
- Creating unit, integration, and contract tests for MCP workflows
- Managing interface changes and ensuring compatibility over time
- Validating releases before rollout and minimizing upgrade risks
- Using practical readiness checks for ongoing support and maintenance
Hands-On Implementation Workshop
- Building a simple enterprise-ready MCP server and client workflow
- Applying validation, resilience, security, and observability best practices
- Reviewing a production readiness checklist
- Planning next steps for adoption within internal teams and platforms
Requirements
- Familiarity with APIs, JSON, and fundamental client-server integration concepts
- Experience with command-line tools, Git, and basic application deployment workflows
- Basic programming experience in Python, JavaScript, or a similar language
Audience
- Software developers building MCP-enabled applications and integrations
- Solution architects and technical leads responsible for enterprise AI integration
- Platform, DevOps, and engineering teams supporting production MCP services
