Image by SciTechDaily
OpenClaw acts like your employee by working on your tasks, generating plans, and supporting you at the company you’re building, the assignment you’re completing, or the meeting you’re planning. It does all of this autonomously. The first versions of OpenClaw were released in November 2025, and by late January to early February 2026, the small project turned into one of the fastest-growing open-source projects in the world. It rose from a couple of hundred stars on GitHub to almost two hundred thousand, and thousands of videos created on YouTube about the power of OpenClaw and automation sparked questions among the developer community: what are these automation systems capable of? How can we use them? How do they work?
In early December 2025, NASA used Anthropic’s AI model Claude to plan a strategic route on the Martian surface. They fed Claude thousands of satellite images to train the model on the best path, optimizing for safety and discovery. Safe to say, Claude was successful, and the rover traveled safely over four hundred meters. From AI-powered navigation systems millions of miles away to autopilots on vehicles here on Earth, it’s reasonable to conclude that autonomy is here to stay and continue improving our lives. But how do they even work? How does an AI know how to do something?
Without complicatingAI with talk of neural networks or math, we can focus on one small yet important part of building tools with AI: MCP servers. The Model Context Protocol is a tool released by Anthropic to speed up the integration of AI into apps and build tools.
Before MCP, if you wanted to automate sending a message on Slack every time there was a new task, you’d have to integrate an LLM like GPT or Ollama with a Slack API. This seems fast and easy, but if you’re building an AI system, having a separate API for every single tool you integrate – Discord, GitHub, Gmail, Google Calendar, and others – is extremely redundant.
The Model Context Protocol, however, acts like a USB-C cable, able to connect with multiple ports with no trouble. You still will have multiple APIs, but the only connection the LLM will have is through the MCP server. Instead of managing hundreds of APIs and individual keys that overload the context of the LLM, there is only one tool the LLM can access if needed.
Take a look at the image below, providing a simple explanation of MCP.
Image by Descope
Now, where are we going with all of this MCP talk? Well, that’s how OpenClaw talks with the apps you use! OpenClaw autonomously calls itself and checks for tasks that need to be done. It’ll look at your Google Calendar and see that you have a test coming up. First, it’ll connect with Telegram (a popular messaging tool for OpenClaw), and it’ll send you a message reminding you about the test. Need help with the topics? Just say, “I don’t really understand this. Can you create me something to help me study?” Within minutes, you will have a comprehensive Google Docs or Notion page with an in-depth lesson on the material. This highlights the power of autonomy?
Setting up OpenClaw is simple: all you need is a machine that is always running if you want OpenClaw to act as a 24/7 AI Agent, always up to something. Typically, you’d use a Raspberry Pi or Mac Mini if you’re feeling extra. However, if you just want to use it while you’re working, you can simply install it on your machine and set it up that way.
We’re moving from tools that wait for our commands to agents that anticipate our needs, plan, and execute tasks independently. As these AI systems become more advanced and more capable of reason, the question is not what will AI automate, but rather how we can adapt to a world where our assistants turn into our collaborative co-workers.
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