Model Context Protocol (MCP): Fundamentals to Production

Learn the MCP architecture—host, client, and server—and how a host runs an AI app, the client connects to a server, and the server wraps tools, APIs, and the file system.

Explore how MCP primitives power production-ready ai workflows by pairing server-side primitives (tools, resources, prompts) with client-side primitives (sampling, illustration, logging).

MCP transport uses json rpc messages across two options: stdio for local development and streamable http for production. It enables offline, same-machine communication and live, distributed updates via server-sent events.

Set up your MCP server development environment across Windows, macOS, and Linux by installing VS Code, Python, Node.js, npm, uv, and the MCP cloud desktop client.

Create and test your first MCP server from scratch, exposing capabilities to AI clients. Validate it with the MCP inspector and connect it to the MCP client Cloud Desktop.

Create your first mcp server with fastmcp in a dedicated workspace, initialize a uv project, set up a virtual environment, install mcp cli, and test the greet tool.

Test and debug your MCP server with the MCP inspector, connecting to observe capabilities, resources, and prompts in real time. Validate stability before integrating with MCP clients like cloud desktop.

Connect your MCP server to cloud desktop and verify end-to-end MCP setup by configuring the cloud client, testing real-time requests, and using model context protocol.

Explore the MCP existing server ecosystem and discover thousands of prebuilt MCP servers you can reuse, with category filters and official, community, and archived options.

Connects the Airbnb MCP server to a cloud desktop MCP client, demonstrates configuration, installation, and queries that extend the llm model with a standardized, secure MCP workflow.

Build an MCP server modeled on CRM system that exposes tools to add a customer, get a customer profile, and retrieve customer orders, with reusable prompts, resources, and streamable HTTP.

Build a production-ready mcp server by initializing a uv project, setting up a virtual environment and environment files, and implementing MCP tools for customer profiles and orders.

Learn to test and run the MCP server in stdio and streamable http modes, using the MCP inspector to verify customer profiles and orders on port 8033.

Implement MCP prompts to standardize enterprise tool usage inside a CRM, ensuring consistency, validation, and scalable AI integration; demonstrates creating, decorating, and testing prompts with the MCP inspector.

Expose structured MCP resources like company leave policies, remote work policies, and audit logs through a secure MCP server to standardize context for production-ready enterprise integration.

Build a custom MCP client from scratch to connect to an MCP server, discover capabilities, and execute requests. Deepen your understanding of client-server interaction and complete the MCP picture.

Build a custom MCP client to connect to the Airbnb MCP server via stdio, list tools like Airbnb search and listing details, and call tools with arguments.

Learn to build a reusable MCP client that connects to an MCP server in stdio or streamable http mode, using a flexible configuration, list tools, and call tools with arguments.

Containerize the MCP server with a Dockerfile and docker-compose to standardize the runtime environment and enable portable deployment, then verify client-server behavior remains unchanged.

Master docker fundamentals, including docker desktop and docker compose, and learn how images create portable containers that run identically on any system.

Create a Dockerfile and docker compose for the MCP project, build and run the container, map port 8003, and validate the setup with the MCP inspector and client.

Review the MCP journey from fundamentals to production, summarizing the model context protocol, architecture, and core primitives: tools, prompts, resources, and transport, plus building, testing, and dockerizing end-to-end MCP workflows.

Extend MCP in real-world systems by adding authentication, databases, third-party APIs, multi-server orchestration, and an AI agent connected to tools and data while implementing logging, security, and cloud deployment.