๐—ง๐—ต๐—ฒ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น ๐—–๐—ผ๐—ป๐˜๐—ฒ๐˜…๐˜ ๐—ฃ๐—ฟ๐—ผ๐˜๐—ผ๐—ฐ๐—ผ๐—น (๐— ๐—–๐—ฃ): ๐—ช๐—ต๐—ฎ๐˜ ๐—ถ๐˜ ๐—ถ๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—ต๐—ผ๐˜„ ๐˜๐—ผ ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ ๐—ฎ ๐˜€๐—ฒ๐—ฟ๐˜ƒ๐—ฒ๐—ฟ

Most AI applications suffer from messy integrations.

You write one connection for a database. You write another for a file system. You write a third for a search index. This makes your code rigid. If you change your AI tool, you must rewrite all your integrations.

The Model Context Protocol (MCP) fixes this.

Think of MCP as a USB-C port for AI. Instead of custom wiring for every data source, you use one standard connector. One server works with any compatible client.

How it works:

MCP uses JSON-RPC 2.0. A client connects to a server using three methods:

Servers provide three main things:

You can build a server quickly using the Python SDK. Here is a simple example using FastMCP:

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Weather Demo")

@mcp.tool() def get_weather(city: str) -> str: return f"Weather in {city}: 22 degrees, sunny"

@mcp.resource("city://{name}") def city_info(name: str) -> str: return "City data goes here"

@mcp.prompt() def travel_plan(city: str) -> str: return f"Plan a trip to {city}"

mcp.run()

Why use MCP?

The biggest advantage is client independence. A single MCP server works with Claude Desktop, IDE extensions, or custom agent frameworks. You do not get locked into one ecosystem.

When to avoid MCP:

Testing your server is easy. Use the MCP Inspector to browse resources and test tools without building a full client.

Source: https://dev.to/tech_nuggets/the-model-context-protocol-mcp-what-it-is-and-how-to-build-a-server-4fbi

Optional learning community: https://t.me/GyaanSetuAi