[Update Course] MCP: Build Agents with Claude, Cursor, Flowise, Python & n8n | Udemy


MCP: Build Agents with Claude, Cursor, Flowise, Python & n8n | Udemy [Update 09/2025]
English | Size: 8.55 GB
Genre: eLearning

AI Automation & Agents with Model Context Protocol – Python, n8n, LangChain, Server, Client, Prompts, Tools & RAG

What you’ll learn
Introduction to the Model Context Protocol (MCP): Practical tips to get started with the course and how LLMs can be extended using tools, prompts, and resources
MCP Basics & Tool Integration in Claude Desktop: Understand the JSON structure, compare server types, set up with Node.js, and install via the MCP Installer
Build Your Own Workflows in Claude Desktop: Access local applications, integrate databases, and connect API keys for secure interactions
Connect MCP with Cursor & Vibe Coding: Install Python via pyenv, understand the Cursor interface, connect to OpenAI or Claude, and use MCPs flexibly
API Keys & Access Control: Setup for OpenAI, OpenRouter & more, understand pricing differences, limitations, and project setup within Cursor
Host Your Own MCP Server in n8n: Install Node.js, cover basics like triggers and actions, understand MCP client vs. host, and configure your server securely
Extend the n8n MCP Server: Connect to Claude, Cursor, or GitHub nodes, integrate Zapier functionality for free, and add your own tools
Integrate Vector Databases into MCP: Manage Pinecone automatically via Google Drive, export workflows, and build RAG agents with vector search
HTTP Integration & GDPR-Compliant Hosting: Send HTTP requests to the MCP server even without an official MCP, learn hosting best practices
Use MCP in Flowise, LangChain & LangGraph: Install Flowise, understand the interface, compare agent platforms, and see real-world use cases
Tool Agents with MCP: Integrate access to emails, calendars, Airtable, web scraping, and Pinecone in Flowise for scalable automation
Flowise AI Agents V2 & New Features: Use LangGraph, work with SQLite as a record manager, and combine tool agents with vector access
Create Specialized Workflows with MCP: Voice control for LLMs, automations in Blender, custom image generation via OpenAI & n8n workflows
Develop Your Own Python MCP Server: Learn server programming basics, understand the GitHub repo, integrate tools, and use the MCP Inspector
Define Your Own Prompt Templates & Resources: Use the modelcontextprotocol Python SDK to manage custom prompts and data structures, and connect them to Claude
Build SSE Endpoints for the MCP Server: Enable real-time connections, trigger custom tools via events, and avoid common server development errors
Understand & Prevent MCP Security Risks: Recognize and mitigate tool poisoning, MCP rug pulls, jailbreaks, and prompt injections with secure strategies
Privacy, GDPR & Legal Frameworks for MCP: Know your rights and responsibilities when hosting, processing data, and using LLM tools in compliance with the law

The Model Context Protocol (MCP) is one of the most exciting new technologies in AI automation and agent development.

Because Large Language Models need more than just prompts — they need context, tools, and external resources.

With MCP, you can provide exactly that.

But how does it work in practice?

How do you build your own MCP servers?

How do you use clients like Claude Desktop, Cursor, Windsurf, n8n or Flowise?

And how can you automate, secure, and integrate it all into your own AI project?

In this course, you’ll learn exactly that – step by step, clearly explained, with many examples and ready-to-use workflows.

Fundamentals: Understand and Use the Model Context Protocol

  • Get a comprehensive overview of the MCP concept, how it works, and where to apply it
  • Learn how tools, prompts, and resources can be connected to LLMs like Claude, GPT, or Gemini using MCP
  • Start with practical tips, materials, and a dedicated course hub full of resources and curated references
  • Understand the key principles of prompt engineering and how system prompts work in the MCP context

Integrate MCP in Claude Desktop & Set Up Your First Servers

  • Install Claude Desktop using Node.js and NVM and configure your first server structures
  • Use JSON files and the official MCP installer to connect tools, databases, or your own APIs
  • Understand different server types (tool servers, prompt servers, database MCPs) and their use cases
  • Connect Claude Desktop with your local system or online services and enable API key–protected access
  • Install Python using pyenv and set up the UV package manager for running your first local MCP server

Combine MCP with Cursor, Vibe Coding & Python

  • Set up Cursor as a flexible client, connect it to existing MCP servers (e.g., Zapier), and explore its limitations and strengths
  • Use Vibe Coding and Python-based configurations to customize your MCP structure
  • Manage API keys efficiently, understand pricing structures, and build your own cross-tool MCP setup

Create, Host & Automate MCP Servers with n8n

  • Learn how to install and configure n8n locally and use it as a full-featured MCP platform
  • Create triggers and actions, and use custom nodes to connect Claude, Cursor, GitHub, or Google Drive
  • Integrate Pinecone and other vector databases for RAG agents directly into your MCP server
  • Learn how to host MCP servers on a VPS and keep them running 24/7 with secure access
  • Use authentication options and GDPR-compliant hosting strategies for secure deployments

Use MCP in Flowise, LangChain & LangGraph

  • Install Flowise and build complex tool workflows (email, calendar, Airtable, web search) using Agent V2
  • Use LangGraph to manage multi-step agent processes with clear role separation and tool execution
  • Manage Pinecone databases via SQLite, combine LangChain functionality, and build scalable automations
  • Explore the Flowise interface and create your own assistants with full MCP integration

Creative Projects & Specialized Workflows with MCP

  • Build voice interfaces for your LLM and control your AI through speech input using MCP
  • Automate 3D workflows in Blender with Claude, Python, and your own MCP server
  • Use the OpenAI API with n8n to generate images automatically
  • Share ideas with the community and explore creative or unconventional use cases

Develop Your Own MCP Servers in Python

  • Learn how to write MCP servers using Python and TypeScript – including prompt handling, tool integration, and resources
  • Use the modelcontextprotocol Python SDK to develop your own Claude-compatible prompt templates
  • Use the MCP Inspector for debugging and diagnostics, and expand your setup with Server-Sent Events (SSE)
  • Understand all transport types for MCP: STDIO, SSE, and Streamable HTTP – when and how to use them
  • Publish your MCP server on GitHub and explore hosting options like Cloudflare, AWS, or Azure
  • Avoid common mistakes and apply best practices for stable, secure server development

Security, Privacy & Legal Foundations

  • Recognize and understand threats like tool poisoning, jailbreaks, prompt injections, and MCP rug pulls
  • Secure your MCP server with API keys, authentication, and proper access control
  • Understand key data privacy regulations like GDPR and the EU AI Act, and address the challenges of hosting generative AI
  • Learn from real-world examples and get clear guidance on how to stay legally and technically compliant

After the course…

You will be able to build, host, develop, and integrate MCP-based agents into tools like Claude, n8n, Cursor, or Flowise.
You will know how to create secure MCP servers, combine them for your own projects, and even offer them as a service.
Whether for business or personal ideas – this course gives you full control over the MCP ecosystem.

Who this course is for:

  • AI developers, tech tinkerers, and automation nerds who want to understand the Model Context Protocol (MCP), build their own servers, or extend existing clients like Claude, Cursor, n8n, or Flowise.
  • Private individuals and AI enthusiasts who finally want to understand how LLMs can be extended with tools, prompts, and resources – and get their first MCP agents up and running.
  • Entrepreneurs and freelancers looking to use MCP-based AI workflows to automate routine tasks, streamline processes, or build their own AI service offering.
  • Software developers & prompt engineers working at the intersection of LLM APIs, tool integration, and workflow automation who want to apply MCP to their own projects.
  • Tech-savvy individuals & AI newcomers who want to combine tools like Claude Desktop, Cursor, n8n, or Flowise and dive deep into the MCP ecosystem.
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