I've seen amazing use cases emerge from combining MindPal + MCPs think appointment-booking chatbots, automated form-filling, or even AI-generated reports saved directly to Google Docs!

But figuring out the exact steps can sometimes slow things down, esp. for people who are new to MCP.

Hey devs

I'm currently working on an LLM agent project integrating with Gmail, Slack, databases...
Every new API integration feels like building everything from scratch

I recently explored something called MCPHubs, which standardizes how agents connect to APIs, files, and databases. It's like cutting down tons of repetitive work.

Now I'm curious

If you're building or using AI agents, connecting them to the tools they need to actually do stuff is critical. MCP (Model Context Protocol) provides a fast & easy way to enable that.

I've spent weeks testing the main players offering hosted MCP servers: @Zapier MCP , @Make MCP, Composio MCP, and @Apify Actor MCP.

I was chatting with an AI community recently, and it struck me lots of people have heard of MCP, but none feel they really get it. Honestly, I can see why. The name itself sounds super technical, right?

But don't let the fancy name fool you! MCP is incredibly powerful and is becoming a really big deal (even giants like OpenAI are jumping on board). I truly believe it's the future for making AI agents actually do things by connecting them to your tools.

@OpenAI just dropped a 34-page guide on how to build intelligent AI agents. It s full of great ideas but could be hard to navigate if you're non-technical. I made a simple, no-code breakdown of only what you need to know here and how to apply these ideas into the no-code AI agents & multi-agent workflows you build on @MindPal here: https://mindpal.space/blog/opena...

I recently came across this Github repo which shares the system prompts of top AI agents on the market: Manus, Cursor, v0, Lovable, Devin, Windsurf, and more.

After studying them, I found there are 5 core principles that underpin all of these system prompts, and make their AI agents so good.

We took a deep dive and broke them all down in a blog post:

https://mindpal.space/blog/decod...

Google just dropped Gemini 2.5 Flash designed to hit the sweet spot for AI agents: speed, cost-efficiency, and solid performance.

It s a leaner, faster, and much more affordable alternative to Gemini 2.5 Pro ($0.15/$0.60 per 1M tokens), while still packing powerful features like multimodal input, a 1M-token context window, and optional thinking for more complex reasoning. Early feedback points to impressive speed, making it ideal for high-volume, cost-sensitive use cases.

AI agents being able to run your Make scenarios are so powerful!! And it's also surprisingly easy to set up, thanks to MindPal's native MCP support and @Make 's recently released MCP server. I made a video to show you how here: https://youtu.be/GskAd_P5Sgc

Hey everyone,

Exciting news! We've just rolled out support for the very latest language models from OpenAI, right inside MindPal. You can now build and experiment with:

• GPT-4.1

• GPT-4.1 mini

• o3

• o4-mini