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MCP Development: Build AI Integrations with Vibe Coding
Rating: 4.0 out of 5(4 ratings)
32 students

MCP Development: Build AI Integrations with Vibe Coding

Master MCP: Build AI-Powered Tools in Python and C# — Connect Claude to Documents, Excel Data, and Local Knowledge Bases
Created byEnrique Galicia, Florencia Perez Vela Quiroz
Last updated 3/2026
English
EnglishSpanish

What you'll learn

  • Build production-ready MCP servers that connect AI assistants (ChatGPT, Claude) to your AEC tools and data sources
  • Implement MCP workflows combining local LLMs and cloud APIs for enterprise-grade AI integration architectures
  • Develop custom MCP tools, resources, and prompts that expose your firm's data and systems to AI assistants
  • Deploy and maintain MCP infrastructure using Vibe Coding methodology—building complex systems with AI collaboration

Course content

4 sections • 33 lectures • 2h 13m total length

  • Introduction2:26

    Enable ai to access external tools and data locally with MCP, a standardized protocol, featuring three ready-made servers for Word, Excel, and private document q&a to extend with ViveCoding.

  • About Myself2:56

    Explore how the model context protocol enables AI to access your documents and data locally. Define how AI calls tools, retrieves results, and builds private integrations with Vive coding.

  • AvantLeap Infrastructure Services0:55

    Explore AvantLeap infrastructure services that accelerate fast prototyping of code, products, and services for digital twins in the IEC industry, enabling sustainability and security AI for real-world impact.

  • The AI Integration Problem3:09

    Bridge the AI knowledge gap with the Model Context Protocol, injecting your proprietary data to improve accuracy, speed, and retrieval in document search, analytics, and RAC.

  • What is Model Context Protocol?4:09

    Discover how MCP, the model context protocol from Anthropic, standardizes AI interactions with tools via cloth and the MCP server, using Studio and HTTP connections to enable reliable data exchange.

  • Setting VisualStudio and Claude Pro3:47

    Install and configure Visual Studio Code and Cloud on Windows, with C#, .NET, and Python tooling. Set up MCP file system, connectors, and Cloud Pro accounts for AI integration work.

  • Adding the Rule Configs and Workflows4:26

    Configure rule configs and workflows to guide MCP server creation with minimal settings and preflight checks, using component types and guardrails through workflows and checklists.

  • MCP Architecture Deep Dive3:31

    Explore the MCP architecture by linking Cloud Desktop to MCP server, using JSON-RPC for requests and responses, and detailing tool definitions, input schemas, and output formats across Studio or HTTP.

  • MCP vs. Traditional API Integration4:41

    Compared to traditional rest-based APIs, MCP enables AI to autonomously access personal data using AI-readable schemas and discoverable tools, reducing cloud reliance and enabling multi-step task orchestration.

  • Understanding MCP2:50

    Explore how MCP powers the file system, and how enabling or disabling MCP components like Windows MCP connects to cloud tools and supports directory operations.

  • MCP Overview3:29

    Discover the MCP protocol foundations, tool definitions, and JSON RPC workflows, including how to read files by path and interpret tool outputs and server responses.

  • VibeCoding an MCP12:27

    Build and run an MCP server with Cloud Desktop, create a Python virtual environment, install fastmcp, and configure cloud connectors to manage nodes and goals.

  • Foundations for MCP

Requirements

  • Solid coding foundation (Course 2) and Vibe Coding experience (Course 3) required—this is advanced technical content
  • Understanding of APIs, web services, and client-server architecture helps but we'll explain concepts as needed
  • Development environment with Python or Node.js ready (we support both FastMCP and TypeScript MCP SDK)
  • Access to AI assistants (ChatGPT Pro or Claude Pro recommended for testing MCP integrations)

Description

Model Context Protocol (MCP) — Connect AI to Your Data

You've probably heard the term MCP. But what does it actually do?

Right now, AI assistants like Claude are powerful in general conversation — but they can't see your files, read your spreadsheets, or work with your private data. Every session starts from zero. MCP is the layer that changes that. It's a standardized protocol that lets AI talk to external tools and data sources. You define what the AI can access. It works locally, privately, on your terms.

Think of it like USB for AI: before USB, every device needed a different connector. After USB, everything just worked. MCP does the same for AI integration — one protocol, any data source.
This is not the last MCP course you'll take — it's the one that makes sure you take the right next one

What You'll Do in This Course

You won't be writing MCP servers from scratch. Three fully working servers are already built and waiting for you as reference — one for Word documents, one for Excel data, one for private document Q&A using a local AI model. What you'll do is run them, understand how they work, and extend them using Vibe Coding.

The course also comes with a complete set of rules, configurations, and Vibe Coding prompts — the same structured approach used to build the servers in the first place. Once you understand the pattern, you have everything you need to build your own integrations.

The Three Servers

- Word Connector — give Claude access to local Word documents: list, search, read, analyze

- Excel Processor — AI-powered data analysis through an HTTP API; works across Python and C#

- Document Intelligence — ask questions about your own PDF and Word files, answered by a local LLM running entirely on your machine, no cloud required

Prerequisites

This course assumes you have coding fundamentals and some experience with Vibe Coding. If you've completed Courses 1 and 2, you're ready. If you come from a coding background and have worked with AI-assisted development, that also works. If you don't have either yet — start there first, then come back. The concepts here build on both.

The Opportunity

MCP is early. Most developers haven't touched it. Most companies haven't figured out what it means for their workflows yet. The window where learning this gives you a real head start is open right now — not for long.

The deeper shift is this: AI that works with your actual data is a different category of tool than AI that answers general questions. When an AI assistant can read your documents, query your data, and work within your specific context — privately, without sending anything to a cloud you don't control — it stops being a productivity trick and starts being a genuine capability multiplier. This course is your entry point to building that.

After This Course:

You'll have 3 working MCP servers, deep protocol understanding, and ability to build any integration needed. You'll be positioned as the MCP expert in your network.

Who this course is for:

  • Professionals building AI-powered tools who need to connect MCP-based assistants to their existing workflows, tools, and data
  • Technical leads introducing MCP into day-to-day practice and needing production-grade integration, not experiments
  • Practitioners who’ve mastered Vibe Coding and want to design MCP systems that reliably connect assistants to internal tools, databases, and APIs
  • System designers and problem solvers who want a practical understanding of MCP to gain leverage in their daily work before it becomes a baseline skill

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