MCP: The USB-C for AI Development (Why You Should Care)

#mcp #ai #learning #todayilearned

Stop Wasting Time on Connectors: Meet MCP

Most devs waste time wiring APIs, plugins, and custom connectors just to get their AI tools working with real data.

Model Context Protocol (MCP) fixes that. Think of it like USB-C for AI: one standard connection that works across editors, repos, and data sources. In seconds, you can hook your LLM into GitHub, Notion, databases, or even your filesystem without reinventing the wheel.

WHY Developers Should Care About MCP (in 10 seconds).

Every dev hits the same pain:

You want your AI assistant to pull issues from GitHub, notes from Obsidian, or queries from Postgres, etc.

MCP kills that pain.

With MCP, you:

Use tools instantly → GitHub, filesystem, databases, all ready-to-plug, etc.
Stay portable → your context follows you across editors, IDEs, and assistants.
Build faster → focus on product, not glue code.

In other words: MCP turns AI assistants into real dev tools, not just chatbots.

What is MCP?

MCP is an open protocol that standardizes how applications provide context to large language models (LLMs). Think of MCP like a USB-C port for AI applications.
Model Context Protocol Docs.

MCP was announced by Anthropic in November 2024 as an open standard for connecting AI assistants to real systems like repositories, business tools, and dev environments Wikipedia.

Its goal is to solve the N×M integration problem: before MCP, every data source needed its own custom connector. With MCP, one standard interface connects everything.

Here’s what MCP brings:

🚀 Pre-built integrations you can plug into instantly.
🔌 A universal connector for building your own tools.
🌍 Open protocol anyone can implement, extend, and share.
🔄 Portability — carry your context and integrations across apps.

If you want to go deeper into the architecture, Check the official overview.

How to Use MCP Servers

MCP servers plug into your AI tools like extensions. Here’s how you can start today:

Claude Desktop

Claude has MCP support built-in. You just drop a claude_desktop_config.json file in the right place.

Quickstart guide .

Example: connect GitHub + filesystem:

{
  "mcp": {
    "servers": {
      "filesystem": {
        "type": "stdio",
        "command": "npx -y @modelcontextprotocol/server-filesystem"
      },
      "github": {
        "command": "github-mcp-server",
        "args": ["stdio"],
        "env": {
          "GITHUB_PERSONAL_ACCESS_TOKEN": "<YOUR_TOKEN>"
        }
      }
    }
  }
}

VS Code / Cursor / Windsurf (with Claude / GitHub Copilot / AI extensions)

Install an AI extension that supports MCP (GitHub Copilot, Claude, etc.)

Open Command Palette → MCP: Add server.
Paste your config.

Choose HTTP, Docker image, or local stdio command depending on the server:

Restart VS Code → your MCP tools show up inside the assistant.

🔌 Connection Types

You can connect servers in 3 ways:

HTTP – some MCPs are hosted in the cloud (easiest). Example:

{
  "servers": {
    "example": {
      "type": "http",
      "url": "https://api.example-mcp.com/" 
    }
  }
}

(Great for quick use, but only trust official/community-verified servers!)
Example: GitHub MCP
"url": "https://api.githubcopilot.com/mcp/"

Docker.
run an MCP server container Locally:

{
  "servers": {
    "github": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "GITHUB_PERSONAL_ACCESS_TOKEN", "ghcr.io/github/github-mcp-server"],
      "type": "stdio"
    }
  }
}

Local build.
build the server binary and point your config to it.
(Setup varies per MCP — check each server’s docs.)