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Duration 14 hours
Course Outline
MCP Fundamentals and Enterprise Use Cases
- Understanding the Model Context Protocol and its position in enterprise AI integration.
- Examining how MCP servers and clients interact with models, tools, and backend systems.
- Exploring common use cases, benefits, and limitations in team-based 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 enhanced maintainability and reuse.
- Implementing validation, consistent output formats, and meaningful error responses.
- Designing workflows that facilitate practical team ownership and support.
Reliability and Security in Production
- Managing failures, invalid requests, and downstream service disruptions.
- Utilizing timeouts, retries, fallback strategies, and safe processing patterns.
- Applying basics of authentication, authorization, and secret management.
- 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, environmental 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 prior to 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 practices for validation, resilience, security, and observability.
- Reviewing a production readiness checklist.
- Planning next steps for adopting MCP within internal teams and platforms.
Requirements
- Understanding of APIs, JSON, and fundamental client-server integration concepts.
- Proficiency in using command-line tools, Git, and basic application deployment workflows.
- Foundational programming experience in Python, JavaScript, or comparable languages.
Target Audience
- Software developers creating applications and integrations that support MCP.
- Solution architects and technical leads overseeing enterprise AI integration.
- Platform, DevOps, and engineering teams responsible for supporting production MCP services.