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Course Outline
MCP Foundations and Enterprise Use Cases
- Understanding the Model Context Protocol 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.
- Reviewing 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 reuse.
- Applying validation, consistent outputs, and useful error responses.
- Designing workflows that are practical for team ownership and support.
Reliability and Security in Production
- Handling failures, invalid requests, and downstream service issues.
- Using timeouts, retries, fallback strategies, and safe processing patterns.
- Applying authentication, authorization, and secret handling basics.
- Supporting 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 compatibility over time.
- Validating releases before rollout and reducing upgrade risk.
- 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 with 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 overseeing enterprise AI integration.
- Platform, DevOps, and engineering teams responsible for supporting production MCP services.
14 Hours