Course Introduction

MCP Overview

- Why MCP matters for agentic AI systems
- MCP clients, servers, tools, resources, prompts, and transports
- How MCP differs from direct APIs, plugins, RAG, and A2A-style communication
- Common use cases for coding agents, enterprise data, workflow tools, and operations
- MCP ecosystem, adoption patterns, and integration tradeoffs
- LAB: Explore MCP basics with a simple studio-based client and server

MCP Clients

- Client responsibilities, per-request metadata, and optional capability discovery
- Invoking tools, reading resources, and using prompt templates
- Integrating MCP clients with LLM applications and agent frameworks
- Handling version compatibility, errors, retries, schema validation, and unavailable servers
- Client-side configuration, permissions, and user approval patterns
- LAB: Create an MCP client and connect it to an LLM workflow

MCP Servers

- Server responsibilities, exposed capabilities, and stateless request handling
- Designing tool schemas, resource interfaces, and prompt definitions
- Mapping local files, APIs, databases, and services into MCP capabilities
- Managing side effects, idempotency, input validation, and output formats
- Packaging and running MCP servers for local and shared environments
- LAB: Build an MCP server that exposes tools and resources

External APIs and MCP

- Wrapping REST, GraphQL, database, and SaaS APIs as MCP tools
- Translating API contracts into safe tool schemas and response formats
- Handling authentication, rate limits, pagination, and partial failures
- Designing least-privilege access for external service integrations
- Choosing between MCP, direct APIs, RAG, and workflow automation
- LAB: Expose a third-party or simulated API through MCP

MCP Security and Authorization

- Trust boundaries between users, clients, servers, tools, and data sources
- Authentication, authorization, least privilege, and secret handling
- Prompt injection, tool poisoning, data leakage, and unsafe action risks
- Approval flows, sandboxing, audit trails, and policy enforcement
- Security review patterns for MCP servers and tool catalogs
- LAB: Add authentication and approval controls to an MCP workflow

Stateless Protocol, Transports, and Extensions

- Per-request protocol version, client metadata, capabilities, and optional server discovery
- Studio and Streamable HTTP transport patterns, including request-scoped responses and cancellation
- HTTP metadata and header-based routing for gateways, policy, and observability
- Explicit application state through tool arguments and server-minted handles, not transport sessions
- Multi-round-trip interactions, long-running work, cacheable catalogs, and extension boundaries
- LAB: Build a stateless MCP interaction with discovery, request metadata, and explicit state

Debugging, Testing, and Observability

- Inspecting request metadata, client-server interactions, tool calls, and resource access
- Testing tool schemas, version compatibility, validation paths, edge cases, and failures
- Debugging transport, authorization, schema, and model-integration issues
- Logging, tracing, metrics, and audit events for MCP applications
- Regression testing MCP integrations as tools, extensions, and models change
- LAB: Debug and instrument an MCP client/server integration

MCP Architecture and Integration Strategy

- MCP reference architectures for local, team, and stateless enterprise deployments
- Integrating MCP with RAG, agent frameworks, AI gateways, and CI/CD workflows
- Versioning, compatibility, extension adoption, catalog management, and lifecycle governance
- Performance, cost, reliability, caching, and operational ownership tradeoffs
- Roadmap planning for MCP adoption across teams and platforms
- LAB: Design a production MCP integration architecture

Model Context Protocol Foundation (RXM404)

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