The "USB-C for AI": A Practical Guide to the Model Context Protocol (MCP)

Why MCP is a Game Changer
Before MCP, we faced the "N x M" problem: you had to rewrite your tool connections every time you switched from Claude to GPT or Gemini. MCP solves this by acting as a universal remote. You build a "Server" once, and any "Client" (like Claude Desktop or Cursor) can immediately use it to read your files, query your database, or check your GitHub PRs.

Building Your First MCP Server in 3 Steps

1. Define Your Tools: An MCP server exposes "Tools"—executable functions that the AI can call.

   
   

2. Standardized Transport:
   Use JSON-RPC 2.0 messages over standard input/output (stdio) for local tools or Server-Sent Events (SSE) for remote ones.
   
   

3. Secure the Context:
   Use Docker to containerize your server, ensuring the AI only sees the specific data you authorize.

   
   

Pro-Tip for 2026:
Use the uv package manager for "zero-install" testing. You can run an MCP server directly from the command line using uvx, making it incredibly fast to prototype utility agents.