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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:
- stdio: Best for local use on one machine.
- SSE: Works across different machines via HTTP.
- Streamable HTTP: Allows two-way data streaming.
Servers provide three main things:
- Resources: Structured data like files or logs.
- Tools: Functions the LLM can run, such as querying a database.
- Prompts: Reusable templates for specific tasks.
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:
- You only need a simple script for one task.
- You need extremely low latency.
- Your data is not for LLM use.
- You only use one specific framework like LangChain.
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