LLM-Powered Automation: The Complete MCP Developer Course

Introduce the model context protocol (MCP) as a universal language that lets AI interact with external tools, data, and other models, turning isolated assistants into collaborative, goal-driven agents.

Join the MCP community of developers, AI enthusiasts, and forward-thinking organizations to build, learn, and push AI boundaries together on GitHub or Discord.

Explore the MCP documentation—from introduction to quick start and tutorials—covering core architecture, LLM integrations, debugging, and how to contribute via the roadmap.

Explore hands-on MCP development by building a Hello World MCP tool and a greeting tool, then connect to a weather API to fetch live data.

Set up the hello MCP tool by cloning the GitHub code, configuring Visual Studio Code, creating a virtual environment, and installing dependencies per the readme to run the project.

Launch the MCP server and explore tools by listing and running a hello world tool, then inspect the code in main.py to see an async decorated tool returning a greeting.

Define a Python function, decorate it with Mcrp two, save the file, open the UI, refresh and connect, then use tools to reveal greetings that output Hello, John.

Build a weather tool that delivers real-time conditions and a five-day forecast by responding to location context passed through mcpp, with a weather API integration.

Connect your weather tool to a live API using Open Weather Map and generate an API key. Sign up, copy the key, and update your VS Code environment.

Set up and launch the MCP weather tool server, connect to the app, and run current weather and forecast for cities like London and Paris, including temperature and humidity.

Explore how weather tool powers MCP integration by building two tools in main.py: get current weather and get weather forecast, using geocoding API, Openweathermap API, and SSE for five-day updates.

Develop context-aware weather tools using model context protocol, fetching real-time and forecast data from Openweathermap API. Build MVP tools: current weather and five-day forecast, that auto respond to location data.

Explore the MCP core components: tools, server, resources, prompts, and context, and how they enable context aware ai. Context includes history, user profile, and memory.

Explore MCP tools, where executable Python functions enable the AI agent to fetch data, perform actions, and automate workflows beyond text. Learn to define tools for the model.

Explore MCP resources, declarative data endpoints for language models, with static and dynamic types. They resemble rest get endpoints and support grounding and prompt augmentation.

Differentiate between MCP tools and MCP resources; tools perform actions and can change state. Provide read-only data with resources, and use tools to act.

Discover how the fast MCP server acts as the brain of your application, managing connections, enforcing protocol compliance, routing requests, and exposing context and tools for the LM.

Explore the MCP client, the active counterpart to the MCP server, which communicates via the Model Context protocol, sends tool calls, receives status updates, and participates in a dynamic loop.

Explore MCP prompts, predefined templates of the model context protocol, to standardize AI interactions and simplify client implementation for document Q&A, summarization, and structured outputs.

Explore the model context protocol's five core components—tools, resources, server, client, and prompts. See how tools perform actions, resources provide data, and prompts standardize workflows.

Explore the MCP tool for GitHub, an MCP server that lets AI models manage repositories, files, and users via the GitHub API, enabling automated code changes and project management.

Install Python 3.7+, git, and a GitHub token; generate and save the token in an env file; set up a venv, start the MCP server, and explore MCP Inspector tools.

Fetch real-time GitHub user profiles with the MCP User Info tool, extracting name, bio, followers, and public repos to power AI workflows and DevOps bots.

Create and delete GitHub repositories using the GitHub API with a dedicated Amcp tool, handling repository name, privacy, and description, via a reusable make request workflow.

Master the file operations tool to create, update, and delete files in a GitHub repository; encode content in base64 and craft commit messages.

Summarizes building GitHub MCP tools that automate repo creation, file operations, and contributor insights, enabling code mentoring, repo cleaning, and self-editing coding assistants.

Build an MCP tool that lets an LLM post tweets, fetch recent tweets, and track username history on X, enabling an AI social media assistant, brand monitoring, and compliance logging.

Learn to set up a modular project structure and manage credentials for X posting, Gemini API keys, and Slack integration, using a requirements file and an environment file.

Explore the tweet posting tool in the MCP inspector, load credentials from .env, and use tweepy to call api v2 create tweet for posting on X.

Track username changes to reveal a social account's history with a mock data tool, enabling security analytics, digital identity tracking, and compliance and reputation monitoring through formatted change data.

Fetch recent tweets tool lets your AI agents pull the latest tweets from public Twitter accounts for real-time content, trend detection, and market monitoring.

Wraps up the module by showing how to empower AI agents with real-time social capabilities through integrating Twitter tools—tweet posting, username history, and fetch recent tweets—into MCP tooling.

Enable your LLM agent to manage emails via the Gmail API, composing drafts, sending messages, and reading Gmail. Label data for organizing or searching, using secure scope access.

Set up a Python-based MCP development environment for Gmail access by installing Python 3.8+, managing dependencies with uv, and configuring a Google OAuth client secret JSON.

Implement secure Google OAuth2 authentication for Gmail access using get_credentials, defining gmail.compose, gmail.send, and gmail.readonly scopes, loading client_secret.json, and storing tokens in token.json for seamless future logins.

Use the Create Draft tool to save an AI-generated email as a Gmail draft with subject, recipients, and body, using Gmail API authentication and drafts.create for review before sending.

Learn how the MCP tool send email integrates Gmail with OAuth to send personalized messages using subject, recipient, and content type, encoding, and the Gmail API.

Learn how the MCP tool Read Labels connects to the Gmail API to fetch label metadata and enable context-aware email automation by filtering, summarizing, and organizing messages.

Fetch Gmail label names with the read label MCP tool, differentiate system and custom labels, and create List Custom Labels to return only user-created labels for smarter email sorting.

Integrate Gmail with MCP tools to enable your AI assistant to read emails, create drafts, send messages, and handle labels, enabling a smart email assistant, summarization bot, and digest scheduler.

Explore no-code automation with generative AI by integrating Zapier and MCP tools, enabling your AI assistant to draft emails and schedule calendar events across apps.

Learn to automate email sending with Claude and Zapier using MCP tools, configure Zapier Actions, generate a URL, and install the Cloud API app for Gmail and Slack notifications.

Configure the MCP action by adding a Gmail draft, connecting a Gmail account, and enabling actions, then bind the MCP endpoint to Zapier.

Demonstrate drafting a Gmail draft via Zapier using the Gmail create draft tool, cloud configuration, and authorization prompts, then verify the draft content about India's capital.

Learn to draft emails with the cursor app by configuring MCP tools, enabling Gmail draft creation, and testing an email about Paris.

Connect a Google Calendar account, add a Google Calendar event via MCP, and enable the action. Run the tool in Cursor to schedule the event and demonstrate automation.

Create an end-to-end LinkedIn post automation tool with the Zapier MCP server, connecting LinkedIn and testing AI-generated content posting to automatically publish.

Explore real-world ai-driven automation with no-code and low-code actions using Zapier MCP. Build automated emails, smart calendar scheduling, and seamless integrations with LinkedIn, Slack, Notion, and Trello.

Learn how Docker solves the 'works on my machine' problem by containerizing your MCP server, delivering portable, isolated, scalable, and secure deployments from development to production.

Install Docker by downloading Docker Desktop from the official site, then run the installer and verify with docker --version and docker run hello world to begin containerizing apps.

Explore Docker's key components: images, containers, dockerfiles, and Docker Hub, and learn how they build, run, and share applications, preparing you to run the MCP Hello World server.

Containerize the hello world mcp tool with a dockerfile, build and run the image, and test it inside a production-ready mcp server for cloud deployment.

Containerise the weather MCP tool with a Dockerfile, install dependencies from requirements.txt, build and run the image on port 6277, and test two tools for current weather and forecast.

Master building and running docker containers with docker build and docker run, using custom dockerfiles, image tagging, and port mappings for clean, interactive deployments.

Learn essential docker commands to manage containers and images, view logs, and access an interactive shell with docker ps, stop, rm, rmi, logs, and exec.

secure your docker environments by using official images, non-root containers, runtime secrets, vulnerability scans, restricted networks, and a read-only filesystem to protect development through production.

Learn how Docker packages MCP projects into portable containers and images, enabling isolation, fast startup, and secure, scalable deployment across laptops and the cloud.

Master MCP fundamentals through five plus hands-on projects, integrating GitHub, Twitter, Gmail, and Zapier, and learn scalable architecture, dockerized deployment, and secure backend automation.