Yuval Avidani
Author
"Most people still use AI like a smart search engine: ask, get an answer, close the tab." That was the comment that caught my attention this week while I was explaining to someone what's changed in Claude, and in my view, that's exactly the gap we need to close. Because while most of us are still just talking to the model, Anthropic has quietly built three layers (MCP, Connectors and Agent Skills) with one goal: turning Claude from someone who talks about work into someone who actually does it.
Let's break this down slowly, concept by concept, without throwing acronyms at you and running away.
The problem we all know: AI that knows everything but can't touch anything
Think about the smartest model you know. It's read half the internet, it phrases things beautifully, it understands context. And still, if you ask it to check what's in your last email, or pull a row from a database, or open a task ticket at work, it's stuck. It knows what to do, but it has no hands.
That was the reality until not long ago. And to solve it, every company built its own bridge: a separate integration (software-to-software connection) for Gmail, a separate one for Slack, another for Google Drive, another for every single tool. Each connection like that is separate code that someone has to write, maintain, and fix whenever something changes. It doesn't scale (it doesn't stretch nicely as you keep adding more tools). It's a jungle of cables, and every cable shaped differently.
The first answer: MCP, the USB-C port of AI
This is where MCP comes in. In plain terms: MCP (short for Model Context Protocol) is an open standard from Anthropic that defines one unified way to connect an AI model to external data sources, tools and services, for anyone building AI products who doesn't want to write a hundred different bridges. Anthropic launched it in November 2024.
Think of it like a USB-C port. Back in the day, every device came with a different charger: one for your phone, another for your camera, a third for your laptop. USB-C said: hold on, let's agree on one plug shape, and any device that speaks this language will just connect. That's exactly what MCP does: one universal socket, and wherever there's a matching plug, the AI connects without custom-built code.
The exciting part is that it's open. Meaning, it's not just for Claude. Anyone building a tool or service can expose it through MCP, and any model that understands the standard can use it. By June 2025, one-click local installation of MCP servers arrived in Claude Desktop, and that turned the whole thing from "a project for developers" into "something you can just flip on."
The second answer: Connectors, the devices that plug into the port
Okay, so we have a port. But an empty port doesn't do anything, you need something to plug into it. That's where Connectors (ready-made connections) come in.
If MCP is the shape of the port, then a Connector is a verified MCP server, a ready-made connection to an external service (like Gmail, Slack or Google Drive) that Anthropic has vetted for security and reliability, for users who want to hook Claude up to their tools without worrying about who wrote the code. They work in Claude.ai, Desktop, mobile and Claude Code, and as of June 2026 there are over 500 of them.
Think of it like the devices we plug into USB-C: headphones, a monitor, a drive. Each one is a Connector. The key difference is that Anthropic reviews and tests them, so we're not plugging some random gray-market cable straight into our machine. That's the difference between "an open standard anyone can implement" and "a list of connections someone actually checked and vouched for as safe."
The third answer: Agent Skills, the recipe books the AI pulls out on its own
Now to the part that excites me the most, because it changes the actual nature of the work. Let's say we've connected Claude to all the tools. It can touch things now. But how does it know when and how to carry out a complex task consistently, time after time, at the same quality?
This is where Agent Skills come in. An Agent Skill is a structured "capability package," a reusable spec that defines how a specific capability behaves and when it kicks in, which Claude discovers and pulls out on its own whenever a task requires it, for anyone who wants consistent results without rewriting the instructions from scratch every single time.
Think of it like a recipe book kept in the kitchen. We don't explain to the chef from zero every morning how to make the sauce: there's a recipe, they know which drawer to reach into, and when. The skill only activates when the task calls for it, so the model doesn't get choked up with instructions irrelevant to the current moment. That's what gives you consistency across research, data extraction, analysis, testing and automation, and it's also what makes maintenance easy: you update the recipe in one place, and it affects every single time that skill gets pulled.
What it looks like when you combine all three layers
Now put the three layers together in your head: MCP gives you the shape of the connection, Connectors give you the actual tools in a verified form, and Skills give you the know-how of working with them consistently. This is exactly the shift from "a chatbot that talks" to "a worker that does things": someone with hands, with access to tools, and with a playbook they know when to open.
In my view, this is also the least understood shift happening in the industry right now, because it doesn't look like a shiny feature. There's no impressive new screen here. There's infrastructure, a quiet layer that changes what you can even ask AI to do. And when the infrastructure is right, suddenly "summarize my week" turns into "go through my emails, cross-reference them with my calendar, open three tasks in the system and draft me a reply," all in one flow.
Why this combo is powerful, and where it breaks
Let's be fair here, because there's no magic involved. The more hands you give AI, the higher the risk: connecting to real tools means access to real data and real actions, and that demands care around security and permissions, which is exactly why Anthropic vets the Connectors in the first place. Other approaches in the market chose to build their own closed ecosystem (a full self-contained environment of tools) instead of an open standard, and that's a legitimate call with its own advantages: tighter control in exchange for less openness. And even with over 500 connectors out there, not every tool exists yet, and not every skill we'd want is already written. This is a young ecosystem growing fast, not a finished product.
Bottom line: these layers don't do any magic, they solve a seemingly-boring engineering problem (how do you connect a model to the world without a jungle of cables) in an elegant way, and that's exactly why they matter so much. So here's the question I'll leave you with: if our AI can already touch our tools and not just talk about them, what's the first thing we'll stop doing ourselves?