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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.
  • Examining common use cases, benefits, and constraints in team-oriented environments.
  • Identifying key design considerations for successful 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 reusability.
  • Implementing validation, consistent outputs, and informative error responses.
  • Designing workflows that are practical for team ownership and support.

Reliability and Security in Production

  • Managing failures, invalid requests, and downstream service issues.
  • Utilizing timeouts, retries, fallback strategies, and safe processing patterns.
  • Applying authentication, authorization, and secure secret handling fundamentals.
  • Ensuring auditability 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, environment variances, and release workflows.
  • Implementing logs, metrics, health checks, and alerting systems for runtime visibility.
  • Troubleshooting common operational issues across clients and backend integrations.

Testing, Versioning, and Change Management

  • Developing unit, integration, and contract tests for MCP workflows.
  • Managing interface changes and maintaining compatibility over time.
  • Validating releases prior to rollout to minimize upgrade risks.
  • Employing practical readiness checks for ongoing support and maintenance.

Hands-On Implementation Workshop

  • Building a simplified enterprise-ready MCP server and client workflow.
  • Applying practices related to validation, resilience, security, and observability.
  • Reviewing a comprehensive 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 MCP-enabled applications and integrations.
  • Solution architects and technical leads tasked with enterprise AI integration.
  • Platform, DevOps, and engineering teams supporting production MCP services.
 14 Hours

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