MCP: Build Agents with Claude, Cursor, Flowise, Python & n8n

Learn MCP basics, set up NodeJS, NVM, cloud desktop, VSCode, and the MCP installer, explore pre-built MCP servers, API-key integration, and key security notes.

Explore updates to the MCP documentation, noting the interface stays the same despite small text changes, while stdio and streamable HTTP remain the primary transport layers.

Explore the Model Context Protocol (MCP) documentation with a fast, holistic overview covering Python SDK, server and client setup, resources, prompts, tools, transports, and common pitfalls.

Install cloud desktop, Node.js, and npm; use nvm to install and switch Node versions, then set up Visual Studio Code and prepare to work with the Model Context protocol.

Explore the cloud desktop interface, enable developer mode, manage MCP servers like blender, and navigate chats, projects, and logs for debugging and workflow setup.

Learn to integrate a file system MCP server into cloud desktop by editing the cloud desktop config, enabling developer mode, configuring windows/macos paths, and testing read and write access.

Discover how to quickly install multiple MCP servers using the MCP installer, edit cloud config files, and troubleshoot with logs to fix time zone and syntax issues.

Install python and the uv package manager to power MCP servers. Verify python version 3.12, install uv via curl or pip, and optionally use pyenv to manage versions.

Explore MCP servers and clients on GitHub and glamma MCP servers, learn to search and compare options, and select safe servers and clients for Claude, Cursor, Flowise, Python, and n8n.

Learn to set up the OpenAI web search API on your MCP server by adding your API key to the cloud desktop config and ensuring correct syntax, free tier considerations.

Navigate unmaintained open-source MCP servers by identifying reference and archived servers such as Brave Search, PostgreSQL, and Puppeteer, and switch or build your own to stay productive.

Explore the Zapier MCP server and its 7000 apps, including tools, resources, and prompts. Learn the main downside of plan limits and a free Cloud Desktop workaround to connect Zapier.

Recap Claude Desktop and MCP basics, including NodeJS and nvm setup, Cloud Desktop with developer mode, and deploying the first MCP server with Python to manage API keys.

Explore cursor, the VS Code extension built on the model context protocol, install it, code in Python, connect to GitHub and Slack, and manage API keys in OpenAI Playground.

Install and explore Cursor, a VS Code-based AI code editor with an agent and models; practice WIP coding using a snake game example to learn project setup and running code.

Connect your Cursor MCP server with Zapier for free, add Gmail and Google Sheets, then auto-run tools to send emails, create sheets, and calendar events, with security reminders.

Create and manage API keys across OpenAI, OpenRouter, and other platforms, understand pricing, and set up projects with budgets and access controls.

Examine the limitations of the model context protocol with Cursor as a client: no access to resources, prompts, discovery, sampling, or routes over time, as updates may expand tool access.

Explore Cursor, a VSCode extension powered by LMS, for easy install, autocompletion, and project templates, then manage MCP servers and API keys for OpenAI and Anthropic.

Set up the addon locally, manage node versions, and learn Mnd-n JSON communication while building an MVP server. Connect Google tools, authenticate, and explore vector databases and chat flows.

Install Nan locally with Node.js to run MCP servers and clients, connect Google Calendar, Gmail, and Google Sheets, and explore the interface and server sent events.

Identify and fix n8n installation errors by selecting the correct node version using nvm for Windows, listing installed versions, installing, and switching versions with nvm use.

Update your local n8n addon by using the Node.js command prompt and npm update -g, restart your local instance, and reload to apply the updates.

Explore n8n basics: design workflows with triggers and actions, connect nodes and models, and build MCP servers and clients using chat, form, mail, and webhooks.

Explore building ai-powered workflows in n8n with built-in templates, editor executions, and new data tables as a google sheets alternative, using llm-driven node assembly and a vector database.

Create your first MCP server in n8n with a no-code setup, enable server-sent events, and connect Google Sheets via Google Cloud Console for LMS workflows.

Connect the MCP server to cloud desktop and cursor by creating a client in NGN with a config file, using ai agents with OpenAI models and Google Sheets.

Secure your MCP server by implementing header authentication, creating credentials, and updating the config file to control access, ensuring only authorized users can view leads and run tools.

Connect the MCP server and client to Zapier for free via Cloud Desktop, then integrate Google Sheets, Google Calendar, and Gmail, and test cross-server triggers.

Connect Google Drive to the n8n MCP server to automatically load PDFs into a Pinecone vector store using embeddings, upserting new files and enabling auto-updated knowledge from Tesla earnings.

Learn to troubleshoot pinecone embeddings by naming the index, using your own vector store, selecting text embeddings from OpenAI, adjusting configurations, and creating the index.

Export and import n8n workflows in json format to reuse a prebuilt workflow by importing the json file into a new MCP workflow canvas.

Connect a Pinecone vector store to the MCP server, configure embeddings, and use memory to enable persistent context; retrieve Tesla financial data via vector search for Q4 2024 insights.

Learn how to integrate multiple vector databases into your MCP server using Pinecone, with separate namespaces and embeddings, to organize knowledge sources across departments and improve answer accuracy.

Learn to connect any API in n8n using the HTTP request node, with weather API calls, get or post methods, a base URL, authentication, and JSON parameters via curl imports.

Learn how to self-host your MCP server so you can access it from anywhere and share it with others, using cloud options like Hostinger or Render and docker-based deployment.

Explore connecting MCP servers to n8n via a community node to list and execute tools from GitHub and Python servers. Set up self-hosted and local deployments.

Assess the limits of the n8n community node, where prompts and resources need a self-hosted server to connect and be useful in MCP, web pages, or AI agents.

Install knn and nodejs, build an mvp server with a trigger and json data flow, and connect tools via a production url. Explore pinecone vector database and hosting options.

Explore the model context protocol in the long chain ecosystem, compare flow wise with the native model context protocol, and build a dual agent with a vector database.

Explore the Lang chain ecosystem, Lang graph, and flow wise for building chat flows and agents, using MCP adapters and the model context protocol across Python and JavaScript.

Install flow wise locally with node.js and nvm, then start the local server on port 3000, and update using npm update -g flow wise.

Explore the Flowwise interface and marketplace, including chat flows, agent flows, tools, and a drag-and-drop canvas. See version two layouts, manage supervisors and workers, and save flows with templates.

Discover the Flowise dual agent, connecting tools and APIs with Compose IO, Brave Search MCP, memory, and tool calling to power complex automation.

Connect pinecone as a vector database within the Flowise tool agent, embed and upsert pdf chunks, and manage vectors with a SQLite record manager for robust retrieval.

Explore Flowise ai agents v2 with MCP, building agent flows, using a chat model, tools, and memory, while comparing to version 1 and deploying with credentials.

Explore integrating custom MCP tools into Flowise by configuring the MCP server and using the super gateway, then connect a Postgres vector store for live knowledge retrieval.

Discover expanded Flowise options in MCP, including embedding chat into HTML or WordPress pages, hosting and security settings, lead capture, prompts, speech-to-text, file uploads, and analytics.

Host your Flowise chatbot on Render with a step-by-step setup, including forking GitHub, configuring environment variables, and embedding the bot in web pages and apps.

Build versatile agents with LangChain and Flowise using MCP, model context protocol, and Python or n8n, connect to a single API, and integrate Pinecone for vector storage.

Discover special MCP workflows and setup tips, and learn to connect diverse tools. Automate blender and generate images with flux API, replicate, OpenAI API, and ChatGPT as an MCP host.

Learn to talk to your LLM and MCP server with voice input and prompt docking. Explore practical tools, Windows H shortcuts, language support, and transcription that works across hosts.

Automate blender with MCP and Claude using Python to connect blender to cloud ai via the model context protocol, enabling prompt-assisted 3d modeling, scene creation, and manipulation.

Demonstrate an MCP server that preserves context across cloud desktop, cursor, and other clients using a pinecone vector database for memory, upserting and querying memories with embeddings and OpenAI model.

Create an MCP server to orchestrate image generation using OpenAI, Flux context models, and other APIs, connecting hosts via a workflow with HTTP requests, base64 handling, and Google Drive uploads.

Learn to use ChatGPT as an MCP host through a webhook workaround when the model context protocol isn't ready. It covers connectors, OpenAI playground, and posting requests to trigger workflows.

Connect Flowise to n8n by importing a curl command and using an http request to trigger a flow from an addon, with bitcoin price testing via brave search api.

Recap: explore building and sharing cool mcp servers and workflows, connect via voice, integrate blender with model context, and generate images, video, and audio using various APIs and local models.

Learn to program MCP servers with Python and cursor, including tools, resources, and a dynamic prompt template, then explore transport options and publish to GitHub.

Explore how to build an MCP multi-tool server with Python code, prompt templates, and GitHub integration, including a calculator, resources, and tools, all orchestrated via cursor and cloud desktop.

Build a basic MCP server in python using the python sdk and cursor, exposing a calculator tool, documenting setup, and testing with the MCP inspector.

Debug and analyze your Python MCP server with the MCP inspector via stdio transport, testing tools, prompts, and resources, and connect across hosts for broader integration.

Learn to create a cloud desktop config file to enable host connections for your MCP server, detailing fields like name, command, arguments, environment, and stdio, and how to test connectivity.

Add and read resources on a Python MCB server with structured URLs, including files, databases, API responses, images, PDFs, audio, video, and livestream data; manage updates via SDKs.

Add a prompt template to your MCP server with the modelcontextprotocol Python SDK, including dynamic prompts and a meeting summary template.

Learn to implement and switch between stdio, streamable http, and server send event for your MCP server, including ssh endpoints, python examples, and integration tips.

Avoid redundant servers and overloading with tools. Keep your server slim and focused with a few well-chosen integrations, and be cautious about hosting and security.

Publish your MCP server on GitHub, and optionally distribute via npm (TypeScript) or PyPI (Python); run with npx, and host on a VM or cloud service.

Host your MCP server on a virtual machine with streamable http, GitHub-based code, and authentication, then compare cloud options like Cloudflare, Render, AWS, and Azure.

Learn to build an MCP server using Claude, Cursor, Flowise, Python, and n8n; index resources, craft focused prompts, debug with the MCP inspector, and deploy securely.

Learn why building your own MCP client is usually unnecessary, since most hosts and frameworks include integrated MCP clients.

Explore security and compliance in this section, including a harmful code demo, llm attacks such as prompt injections, data poisoning, jailbreaks, and guidance on keys, access, data privacy, and licenses.

This lecture shows the dangers of plugging arbitrary internet servers into cloud desktop configs, highlighting potential malicious code and data exposure and advising verification of official servers.

Explore tool poisoning, MCP rug pulls, and related security vulnerabilities in MCP servers, including shadow tool descriptions, cross-server risks, and practical mitigations like tool pinning and clear UI patterns.

Explore common LLM attacks - jailbreaks, prompt injections, and data poisoning - and their impact on MCP servers, especially when internet access or vision features are involved.

master secure api key handling and authentication for mcp servers, never exposing keys, storing them safely, deleting before publishing, rotating keys, and using authentication with bearer tokens or headers.

Avoid giving your Amqp server excessive access to API keys, local files, or Gmail; limit permissions to prevent accidental deletion and data exposure when integrating with cloud desktop environments.

Explore copyrights, data privacy, censorship, licenses, and compliance for building MCP servers and AI agents via APIs, including OpenAI and Flowise licensing and EU regulations.

Learn how the model context protocol connects clients to servers, standardizes http requests, and supports prompts, resources, and multiple MCPs with cursor and flow wise.