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Course Outline

MCP Foundations and Enterprise Use Cases

  • Understanding what the Model Context Protocol is and its role in enterprise AI integration.
  • Exploring how MCP servers and clients interact with models, tools, and backend systems.
  • Identifying common use cases, benefits, and constraints in team-based environments.
  • Highlighting key design considerations for adopting MCP in production.

Designing MCP Servers and Clients

  • Defining capabilities, contracts, and clear responsibilities between server and client components.
  • Structuring tools, resources, and prompts to ensure maintainability and reusability.
  • Applying validation techniques, ensuring consistent outputs, and providing useful error responses.
  • Designing workflows that facilitate team ownership and support.

Reliability and Security in Production

  • Managing failures, invalid requests, and issues with downstream services.
  • Utilizing timeouts, retries, fallback strategies, and safe processing patterns.
  • Implementing basics of authentication, authorization, and secret handling.
  • 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 maintaining compatibility over time.
  • Validating releases before rollout to minimize 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 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 utilizing command-line tools, Git, and basic application deployment workflows.
  • Basic programming proficiency in Python, JavaScript, or a comparable language.

Audience

  • Software developers creating applications and integrations enabled by MCP.
  • Solution architects and technical leads responsible for integrating enterprise AI.
  • Platform, DevOps, and engineering teams supporting production MCP services.
 14 Hours

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