MCP (Model Context Protocol) Explained — The Universal AI Tool Standard
MCP — The Universal AI Tool Standard
Model Context Protocol (MCP) is an open standard created by Anthropic that standardizes how AI models connect to external tools and data sources. It's like USB for AI — one protocol to connect to everything.
The Problem MCP Solves
Before MCP, every AI tool integration was custom. Want your agent to access a database? Write custom code. Want it to search the web? Different custom code. Want it to read files? Yet another integration. MCP provides one standard protocol for all of these.
How MCP Works
MCP uses a client-server architecture. The AI model (client) communicates with MCP servers that expose tools. Each server can provide multiple tools, and you can connect multiple servers to one AI model.
Building an MCP Server
from mcp.server import Server
from mcp.types import Tool, TextContent
server = Server("my-tools")
@server.tool()
async def query_database(sql: str) -> str:
"""Execute a read-only SQL query against the database."""
results = db.execute(sql)
return json.dumps(results)
@server.tool()
async def create_ticket(title: str, description: str, priority: str) -> str:
"""Create a support ticket in the ticketing system."""
ticket = ticketing.create(title=title, desc=description, priority=priority)
return f"Created ticket #{ticket.id}"Why MCP Matters for Agent Developers
- Reusability — Build a tool once, use it with any MCP-compatible AI
- Ecosystem — Growing library of pre-built MCP servers
- Security — Built-in permission model
- Composability — Mix tools from different servers
Getting Started
Our course covers MCP in detail in Lesson 4.8, including building servers from scratch, connecting to Claude Desktop, and production deployment patterns.