MCP Workshop

MCP Workshop – A Hands‑On Lab Suite for Model Context Protocol

The MCP Workshop is a curated set of practical labs that guide developers through the full lifecycle of building, testing, and deploying Model Context Protocol (MCP) servers. By providing a step‑by‑step progression—from a single “Hello Claude” server to a multi‑server retail scenario and finally to cloud deployment on AWS Fargate—this workshop demystifies how MCP servers can be integrated into real‑world AI workflows. The goal is to equip teams with the knowledge and confidence needed to create portable, scalable AI tooling that can be plugged into any host application or AI model.

At its core, the workshop solves a common pain point: how to expose domain‑specific functionality to an AI model in a standardized, machine‑readable way. Traditional approaches often involve custom SDKs or ad‑hoc APIs that lock the model into a particular ecosystem. MCP, however, defines a clear client–server contract that separates the AI’s decision logic from external capabilities. The workshop demonstrates this by showing how a simple server can offer tools (e.g., searching, calculation), resources (structured data via URI templates), and contextual prompts—all of which the AI can invoke on demand. This separation lets developers iterate rapidly on either side without breaking the other, fostering a clean development lifecycle.

Key features explored in the labs include:

• Tool execution: The AI can call server‑provided functions with typed arguments, receiving structured responses that can be used directly in subsequent reasoning steps.
• Resource access: URI templates allow the server to expose data endpoints that the AI can query, enabling dynamic retrieval of up‑to‑date information.
• Context injection: Custom prompts or environmental data can be supplied to the AI, enriching its responses with domain knowledge.
• Multi‑server orchestration: Lab 02 shows how several MCP servers can collaborate—each handling a distinct domain (inventory, pricing, logistics)—and how the AI coordinates calls across them.
• Cloud readiness: Lab 03 demonstrates deploying these servers to AWS Fargate, configuring HTTPS, and enabling streaming responses, ensuring the solutions are production‑grade.

Real‑world scenarios that benefit from MCP include e‑commerce assistants that need live inventory checks, financial bots that perform calculations on the fly, or customer support agents that pull ticket data from internal systems. By integrating MCP servers into an AI workflow, developers can maintain a clear boundary between the model’s reasoning and external services, leading to more reliable, auditable, and maintainable solutions.