Introduction
Artificial Intelligence (AI) is no longer just about building smarter models—it’s about connecting those models to real-world tools and data. That’s where the Model Context Protocol (MCP) comes in.
Think of MCP as the USB-C of the AI world—a standard way to connect AI models with external systems. Whether it’s files, APIs, databases, or custom tools, MCP provides a consistent, reusable approach.
Why Do We Need MCP?
Today, AI applications often struggle with:
Isolated models → trained once, but outdated quickly.
Custom integrations → each data source or tool needs bespoke code.
Scaling problems → more tools = more complexity.
Context gaps → AI forgets state across workflows.
MCP solves these by standardizing how models discover, connect, and use tools/data.
MCP Basics – How It Works
At its core, MCP has two main actors:
Client (AI App / Assistant): e.g. Chatbot, IDE plugin, desktop AI app.
MCP Server: exposes resources, tools, and prompts.
Flow:
- Client connects to MCP server.
- Server shares what’s available (tools, resources, prompts).
- Client requests usage (e.g., “fetch user data”, “run SQL query”).
- Server executes and returns results.
- AI integrates the result into conversation or workflow.
Core Concepts
Resources → files, databases, APIs, or documents the AI can read.
Tools → actions the AI can perform (search, call APIs, run functions).
Prompts → reusable templates or workflows.
Transport → how clients/servers talk (SSE, HTTP, stdio).
MCP vs Traditional APIs
Feature Traditional APIs MCP
Integration effort High (custom code per API) Low (standard interface)
Reusability Low High
Context handling Manual Built-in
Discovery Rare Native
Scalability Poor Excellent
MCP is like moving from dozens of custom adapters → to one universal connector.
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