The Complete Model Context Protocol (MCP) Masterclass

Explore how the model context protocol lets large language models access external information through tools and prompts. Break down model, context, and protocol and their roles in standardized communication.

Explore how servers and clients communicate over networks via the client-server model, using protocols like HTTP and FTP, and how the MCP context protocol fits in.

Standardize communication between AI models and data stores, CRMs, and more with MCP, the universal adapter for AI applications, reducing complexity from m×n to m+n with reusable MCP servers.

Activate MCP to extend LLM knowledge beyond cutoffs by injecting context, reducing hallucinations, and bridging isolated intelligence to real-world tasks through tools and databases. Enforce a single standardized protocol to replace custom glue code.

Enable MCP to empower LLMs and AI agents with secure personalization, specialized knowledge, and access to tools via MCP servers, while enforcing protocol-level consent and robust data privacy.

Explore how anyone can create an MCP server to wrap access to department services, detailing tool schemas, functions, templates, and the distinction from direct API calls.

Map the MCP architecture from host and MCP client to the MCP server, then explore the server core, including protocol handler, transport layer, capability engine, security layer, and data protection.

Learn how MCP hosts run a client that connects to MCP servers to access tools, resources, and prompt templates through a bidirectional 1-to-1 protocol, grounding the AI model with context.

Explore the MCP transports, a delivery system for MCP protocol messages from client to server, through JSON-RPC formats over stdio, SSE, or HTTP, with local vs remote trade-offs.

Compare the three MCP transports—standard io, server-sent events, and streamable http—and highlight their local performance, real-time streaming, and universal compatibility for MCP servers.

Learn how server-sent events enable real-time streaming for web apps, chat, and live dashboards over http, with automatic reconnection and browser support.

Streamable HTTP enables streaming responses and continuous data delivery over HTTPS, balancing universal compatibility with enterprise scalability and seamless integration with load balancers, CDNs, and API integrations.

Explains the MCP communication lifecycle from initialization to termination between client and server. Contrasts stdio transport with streamable http, highlighting stateless and stateful streaming for remote servers.

Explore the architecture of the MCP collaborative ecosystem, showing how the MCP protocol connects hosts and clients to local files, databases, and external APIs for dynamic, context-driven AI actions.

Install and configure the required dev tools to build MCP servers: VS Code, Python with UV, Clod Desktop as an MCP host and client, and Node.js.

Run your first community MCP server on Claude Desktop and explore the Model Context Protocol’s open integration for LM applications with actor-critic thinking and dual-perspective analysis.

Set up a python project with uv and a virtual environment for an MCP server. Test with the MCP inspector over stdio and decorate a function as an MCP tool.

Add and test a get random nickname tool on the MCP server by combining a random adjective with a name from a user-provided or default list, and verify results.

Test the MCP server in Claude Desktop by configuring the recipe server, installing dependencies, and setting the working directory and python path; prepare for remote deployment via streamable http.

Create a local SQLite MCP server to read a SQLite database, explore tables, and fetch top chatters as a reusable MCP tool.

Develop a get countries tool on the SQL MCP server to query the world DB, converting sqlite rows to dictionaries with ISO codes, capital, currency, and emoji.

Add and test tools for the MCP server to search countries, cities, regions, and currencies against the world db, including country code lookups and region queries.

Install and test the world SQL MCP server in Claude Desktop and cloud desktop, verify with the MCP inspector, and run currency, region, and city queries.

Learn to run an MCP server locally in Visual Studio Code with Copilot, configure Python path and SQLite server, and query countries, cities, and regions with built-in MCP tools.

Explore MCP primitives: tools, prompts, and resources that feed real-time data to language models. Build contextual prompts and leverage a prompt library for data sources like JSON and API responses.

Build and test an MCP server focused on prompts, using Python to define prompts with a get_prompt function, explore the MCP dashboard, and deploy via cloud desktop for broad access.

Explore how to build MCP resources for a library management system, returning a full catalog as JSON via a URI, using a mock database and resource annotations.

Learn how to add and test MCP resources in Claude Desktop, filtering books by category, creating new endpoints, and distinguishing resources from tools to build library data flows.

Test streamable http transport by spinning a local server, wiring a stream tester to MCP endpoints, and verifying remote MCP access through cloud desktop and npm MCP remote.

Build an end-to-end MCP server for a recipe bot, set up a virtual environment, install dependencies, query the mail DB API, and save recipes to JSON.

Add and test new tools to the recipe MCP server, including get recipe details and create meal tool, to build meal plans and save recipe data as JSON.

Add resources to the MCP server by switching from tool to resource, exposing resources for cuisines, recipes, meals, and stats, and generating markdown files with ingredients, summaries, links, and images.

Assemble MCP prompts with predefined templates for recipe search, meal planning, cooking lessons, ingredient exploration, and cultural cuisine, interpolating cuisine and recipe counts to produce complete prompts.

Test and debug the recipe MCP server on cloud desktop by installing with the MCP CLI, configuring cloud settings, running random name and nickname, and observing security prompts and docstrings.

Prepare the deployment environment, configure port 8000 and runtime, push the MCP server to GitHub, and deploy on render to validate cloud access and MCP interactions.

Learn to install and test a remote MCP server via Cloud Desktop, create a custom connector, and configure tools, prompts, and resources for end-to-end MCP workflows.

Learn to run a remote MCP server from VS Code by adding the MCP server, configuring the recipe server final, and exploring resources, prompts, and recipe retrieval.

Wrap up and next steps for the MCP masterclass: practice building MCP servers, stay updated on moving MCP developments, and explore the GitHub and docs to advance your skills.