What is MCP?

The Model Context Protocol (MCP) is Anthropic's open standard for communication between AI models and external tools, data sources, and services. MCP solves the fundamental problem of tool integration: every AI provider, every framework, and every tool speaks its own language. MCP creates a universal interface.

The Problem Without MCP

Without a unified protocol, every integration must be built individually:

Claude ←→ Custom Integration ←→ Slack
Claude ←→ Custom Integration ←→ Jira
Claude ←→ Custom Integration ←→ Database
GPT ←→ Different Custom Integration ←→ Slack
GPT ←→ Different Custom Integration ←→ Jira

Result: N models × M tools = N×M integrations. That doesn't scale.

The MCP Solution

MCP defines a standardized protocol:

Claude ←→ MCP ←→ Slack MCP Server
GPT ←→ MCP ←→ Slack MCP Server (same server!)
Gemini ←→ MCP ←→ Slack MCP Server (same server!)

Result: N models + M tools = N+M integrations. That scales.

Client-Server Architecture

MCP Host

The application where the AI model runs (e.g., Claude Desktop, IDE plugin, custom app).

MCP Client

The component in the host that establishes and manages the MCP connection to the server.

MCP Server

A lightweight service that provides specific capabilities via the MCP protocol:

┌────────────────┐     ┌──────────────┐     ┌────────────────┐
│   MCP Host     │     │  MCP Client  │     │  MCP Server    │
│  (Claude App)  │────▶│  (Protocol)  │────▶│  (Slack API)   │
└────────────────┘     └──────────────┘     └────────────────┘

What Can an MCP Server Provide?

Capability	Description	Example
Tools	Functions the model can call	Send message, create ticket
Resources	Data the model can read	Files, database entries, API data
Prompts	Predefined prompt templates	Analysis prompt, summary prompt

Why MCP for Interoperability?

Open Standard

MCP is an open protocol — not proprietary. Anyone can build MCP servers and clients.

Growing Ecosystem

Hundreds of MCP servers already exist: filesystem, GitHub, Slack, Google Drive, databases, browser control, and many more.

Future-Proof

Instead of building proprietary integrations, you invest in a standard supported by the entire AI industry.

MCP vs. Function Calling

Aspect	Function Calling	MCP
Scope	Single model	Model-agnostic
Discovery	Manually defined	Automatic
Standard	Provider-specific	Open standard
Reusability	Per integration	Build once, use everywhere

Practical tip: MCP doesn't replace function calling — it complements it. Function calling is the foundation, MCP is the standardization. Build new tool integrations directly as MCP servers — they'll instantly work with any MCP-capable client.