You have probably heard the term MCP thrown around in AI circles lately. Maybe you saw it in Claude's settings. Maybe a developer mentioned MCP servers. Maybe you are just wondering what it stands for.
Here is the short version: MCP (Model Context Protocol) is the USB standard for AI.
Before USB, every device had its own proprietary cable. Printers, cameras, keyboards — all different connectors. USB standardized everything. Plug in any device, it just works.
MCP does the same thing for AI tools. Before MCP, every AI-to-tool connection was custom-built. A GitHub integration for ChatGPT did not work with Claude. A database connector for one tool was useless in another. Every connection was bespoke.
MCP changes that. Build one integration, and it works everywhere.
Why Should You Care?
Without MCP, AI is a question-answering machine. You paste text in, it generates text back. Useful, but limited.
With MCP, AI becomes an action-taking assistant. It can:
- Read and write your files — "Find all TODO comments in my project"
- Query your databases — "How many users signed up this week?"
- Manage your code — "Create a PR that fixes the bug in issue #42"
- Send messages — "Post the standup summary to Slack"
- Browse the web — "Check if our pricing page is live"
- Remember things — "Remember that we chose PostgreSQL for analytics"
The difference is night and day. AI goes from a smart text box to a capable coworker.
How MCP Works (No Jargon)
MCP has three parts:
1. Hosts — The AI application you are using (Claude Desktop, Claude Code, VS Code). The host is what you interact with.
2. Clients — The connection layer inside the host that talks to servers. You do not interact with clients directly. They handle the plumbing.
3. Servers — Small programs that give AI access to specific tools. A GitHub server lets AI manage repos. A Slack server lets AI send messages. A file system server lets AI read your files.
When you ask Claude to "check my latest Git commits," here is what happens:
- Claude (the host) sends your request to the MCP client
- The client finds the right server (Git server)
- The server executes the request (reads Git log)
- Results flow back through the client to Claude
- Claude interprets the results and responds to you
You never see steps 2-4. You just ask, and Claude does it.
What MCP Servers Exist?
Hundreds. Here are the ones that matter most:
File System — Read, write, search, and manage files on your computer. This is how Claude Code edits your codebase.
GitHub — Create PRs, manage issues, review code, search repos. Turns AI into a junior developer.
Slack — Read channels, send messages, search history. AI can participate in your team communication.
PostgreSQL / Supabase — Query databases, run SQL, manage data. AI becomes a data analyst.
Brave Search / Web — Search the internet and fetch web pages. AI can research in real-time.
Google Drive — Read and search documents. AI can reference your entire doc library.
The full list grows every week. The MCP ecosystem is the fastest-growing part of the AI tool landscape right now.
Who Built MCP and Why?
Anthropic (the company behind Claude) created MCP and released it as an open standard in late 2024. The key decision was making it open, not proprietary.
This matters because:
- Any AI provider can implement MCP (and many have)
- Developers build one server that works everywhere
- No vendor lock-in — switch AI providers without rebuilding integrations
Compare this to OpenAI's function calling, which only works with ChatGPT. Or Google's extensions, which only work with Gemini. MCP is the first universal standard, and that is why it is winning.
How to Start Using MCP
If you use Claude Desktop:
- Open Settings → Developer → Edit Config
- Add MCP servers to
claude_desktop_config.json - Restart Claude — the tools appear automatically
If you use Claude Code:
- MCP servers are configured in your project's settings
- Many servers work out of the box (file system, Git)
- Add more with
claude mcp add
If you want to build your own: A minimal MCP server is about 15 lines of TypeScript. You create a server, register tools with descriptions, and start listening. Claude discovers the tools automatically.
The barrier to entry is surprisingly low. If you can write a basic API endpoint, you can build an MCP server.
MCP vs Function Calling vs Plugins
Function Calling (OpenAI) — Define functions in your API call. The model returns structured JSON saying which function to call. You execute it yourself. Model-specific and requires API access.
Plugins (Deprecated) — ChatGPT's original attempt. Required hosting an API with a specific manifest. Shut down in favor of Custom GPTs and Actions.
MCP — Universal protocol. Works across models and clients. The server handles execution, not you. Secure by design — the model never has direct access to your systems.
MCP is the evolution. It learned from the limitations of function calling and plugins and built something that scales.
Is MCP Secure?
Yes, by design. Three key principles:
-
Separation — The AI model never directly accesses your data. It sends structured requests through MCP, and the server decides what to allow.
-
Permissions — Each server defines exactly what it can do. A read-only file server cannot delete files. A GitHub server can be configured to only read, not write.
-
Local execution — Most MCP servers run on your machine. Your data never leaves your computer unless you explicitly configure it to.
The security model is "the server is the gatekeeper." The AI asks. The server decides.
What is Next for MCP?
MCP adoption is accelerating. VS Code, Cursor, Windsurf, and other developer tools now support it. Non-developer tools are starting to implement it too. The pattern of "AI + tools = agent" is becoming the default architecture.
If you work with AI in any capacity — developer, business owner, marketer, creator — understanding MCP is becoming as fundamental as understanding APIs was ten years ago.
Want to go deep? Our MCP & AI Tool Integration course covers everything from architecture to building production servers. First 3 lessons are free.