Mastering MCP: Beginner to Pro Across the AI Ecosystem

Explore the MCP architecture within the AI ecosystem and its role in a beginner to pro journey across the course.

Explore the MCP architecture, a flexible client-server bridge built on json-rpc 2.0, with core components server, client, and transport layer, and transport options like stdio and streamable http.

Mastering MCP presents a client-server architecture where MCP clients initiate requests via a json rpc protocol, routing queries to servers that access local, remote, or web api data sources.

Understand the MCP client–server architecture, where the server exposes tools, resources, and prompts, the client queries resources and invokes tools, guided by structured prompts and a clear flow.

Explore the MCP transport layer that converts MCP protocol messages to JSON RPC 2.0 and back, enabling reliable communication, standardizing the API, and supporting stdio, http, and SSE transports.

Explore how the MCP transport layer uses stdio for standard input/output to enable lightweight local communication in CLI tools, shell scripts, and desktop integrations.

Explains server sent events (sse) in the model context protocol (mcp), enabling server to client streaming over http for real time stock price updates and simple notifications in restricted networks.

Discover how streamable http replaces server sent events with a single endpoint that handles post and get, enabling real-time bidirectional json-rpc streams and secure ai integrations.

Master MCP from beginner to pro across the AI ecosystem, exploring how Claude Desktop supports MCP mastery.

Discover how Claude Desktop, built on the MCP (model context protocol), is downloaded and used on macOS, Windows, and Arm64, with a Google login for quick access.

Demonstrates building your own MCP server with UV, setting up in Visual Studio Code, installing the MCP CLI, and connecting the server to Cloud Desktop for practical integration.

Explore how to build a fast MCP server with a tool called what am I learning, using a decorator to register the MCP tool, and enable client interactions over stdio.

MCP inspector is an interactive web-based dev tool for testing and debugging model context protocol servers, built with React, offering live exploration of resources, prompts, tools, and logs.

Connect your MCP server to Claude Desktop by configuring cloud settings, adding the server path, and exposing a tool to demonstrate tool calling and learning prompts.

Run MCP install learning dot pi to automatically update config.json in cloud config, adding the learning info server without manual edits, then verify in Settings, developer, my learning, edit config.

Identify the essential prerequisites for lab exercises in the Mastering MCP course, ensuring learners build the foundational skills required for hands-on AI practice across the ecosystem.

Set up Visual Studio Code on Mac, install it, open the terminal, create folders, and install UV for Python virtual environments.

Learn to set up Azure OpenAI, create a resource and deployment for a base model like GPT-4 mini, and retrieve the endpoint and API key.

Install and verify Azure CLI on Windows, macOS, and Linux to enable authentication for the MCP server, using winget, brew, or curl and az commands.

Master how to build a robust MCP server by integrating Langchain, Gemini, and SQLite to enable scalable AI workflows across the MCP ecosystem.

Explore the long chain MCP adapter, which connects to an MCP server like a SQLite database, enabling natural language queries and tool wrapping for long chain and Lang graph.

Translate a natural language query into SQL using the MCP SQLite server. See how the Lang Chain MCP adapter enables Gemini to fetch tabular results.

Identify and set up prereqs for MCP: Python 3.8+, git to clone the SQLite MCP server, a Google API key for Gemini, and Node.js with npm, plus the database.db file.

Install the uv package manager, create the MCP sqlite lang chain project, and install dependencies including MCP, long chain adapter, lang chain, and Google Gemini Gen I, with virtual environment.

Set up the NCP SQLite database server by cloning SQLite repository, then configure Google API key in a dot env file and prepare the environment for Gemini 2.0 flash llm.

Explore the file system MCP server workflow, using Lama index to orchestrate tool calls via the model context protocol with GPT four mini on Azure OpenAI, returning results.

Prioritize prereqs like Python 3.8+, Node.js with npm, npx, and Azure OpenAI API access, then learn to obtain endpoints and API keys for your code.

Set up the environment by creating a llama index MCP folder in Visual Studio Code, then initialize with uv init lama mcp and install llama index, Azure OpenAI packages.

Demonstrates configuring a dot env with Azure OpenAI keys, endpoint, and version, deploying GPT-4 mini, and walking through the MCP tooling and memory setup in code.

Explore how Azure MCP server enables natural language interaction with Azure services through the OpenAI SDK, offering read-only operations for storage and cosmos db, with future read-write capabilities.

Understand how a user query travels from the OpenAI agents SDK to the GPT model, triggers tool calls via the model context protocol, and executes Azure commands.

Initialize the azure mcp assistant project environment in visual studio code, install the package manager, and add dot env, open ai agents, and mcp cli packages.

Validate the environment file and OpenAI API key, confirm the correct Python environment, then perform Azure CLI login and subscription selection before running the code.

Learn to use the OpenAI agents library to build an async AI assistant with an agent and runner, environment loading, and an Azure MCP server for tool access.

Run the Azure MCP demo by executing the code in Visual Studio Code to start the MCP server and query storage accounts and resource groups in a formatted table.

Explore PayPal's MCP server from the remote mode using server-sent events, learn its tools, and see how to create a simple invoice via natural-language input.

Explore the workflow and tech stack using Google ADK, Gemini LLM, and the PayPal MCP server to generate an invoice from natural language.

Set up python 3.9+ and nodejs, configure a PayPal account with 2FA, initialize a google adk project, and prepare PayPal_agent with init.py, agent.py, and .env.

Launch the code with the ADK web interface, run a local server, and have the PayPal agent create an AI enablement invoice and email it.

Build your own MCP server as part of mastering MCP across the AI ecosystem, designed for beginners to pros.

Meet prereqs: Python 3.10+, Azure CLI with az login, and the UV package manager. Create a Visual Studio Code project folder and add Azure server.py with the environment file.

Set up your MCP project structure with a dot env file, configure Azure subscription using az login, and add the Azure Resource Group server.py to run Python code.

Build a lightweight MCP server to authenticate with Azure, load env vars, and create or update an Azure resource group with tags using the Azure SDK.

Start the MCP inspector, connect to the running server, and test the Create Resource Group tool to deploy an Azure resource group and verify it appears.

Demonstrates testing an MCP server via cloud desktop, using MCP install to generate config.json, then calling the external integration to create an Azure resource group in East US two.

Build your own MCP server using S-HTTP and advance from beginner to pro across the AI ecosystem.

move from stdio to streamable http, expose the mcp server on 0.0.0.0:8000, and test with the inspector, using the same Azure resource group server and remote deployments.

Start the MCP server and test with the inspector to verify streamable http on uvicorn 0.0.0.0:8000. Demonstrate creating a local resource group and plan production deployment on VM in Azure.

Provision a new Azure virtual machine by selecting resource group, region, availability zone, Ubuntu image, and SSH key, then review and create with port 22 open for MCP server.

Register an Entra ID app, generate a client secret, and enable service principal authentication for the MCP server; then assign contributor access via IAM.

Create and push a GitHub repository from your local files. Then clone it to an Azure VM to populate the MCP directory, while avoiding environment files.

Configure an environment file with azure credentials from app registration, load it in the code, and start the MCP server remotely with uvicorn; prepare to connect via inspector to test.

Test a cloud-based MCP server running in an Azure VM from your local machine using the inspector to connect via IP: 8000 and validate remote resource creation.