What is MCP?¶

The Model Context Protocol Explained¶

The Model Context Protocol (MCP) is an open standard that enables seamless communication between AI models and external tools, data sources, and services. Think of it as a universal adapter that allows AI assistants to interact with any system in a standardized way.

Definition in Plain English¶

MCP is a protocol that lets AI models use tools and access data through a consistent interface, regardless of the underlying implementation.

Just like HTTP standardized web communication, MCP standardizes AI-to-tool communication. Any AI model that speaks MCP can interact with any tool that implements MCP, creating a truly interoperable ecosystem.

The Analogy That Clicks¶

Imagine you're traveling internationally with your devices:

• Without MCP: You need different adapters for each country (custom integrations for each AI platform)
• With MCP: You have one universal adapter that works everywhere (one protocol for all integrations)

Core Components¶

1. Clients¶

The AI applications that want to use external tools and data. Examples: - AI assistants like Claude - Custom AI agents - Development tools with AI features

2. Servers¶

Services that expose tools and data to AI clients. Examples: - Database connectors - File system interfaces - API gateways - Calculation engines

3. Transport¶

The communication channel between clients and servers: - Standard I/O (stdio) for local connections - HTTP/WebSocket for network connections - Custom transports for specialized needs

What Makes MCP Special?¶

🎯 Purpose-Built for AI¶

Unlike general APIs, MCP understands AI-specific needs: - Tool discovery and description - Parameter validation - Context management - Streaming responses

🔧 Standardized Operations¶

Every MCP server supports the same core operations:

# Discover available tools
tools = await client.list_tools()

# Get tool descriptions
schema = await client.get_tool_schema("calculator")

# Execute tools with parameters
result = await client.execute_tool("calculator", {
    "operation": "multiply",
    "a": 7,
    "b": 6
})

🌐 Language Agnostic¶

MCP uses JSON-RPC 2.0 over standard transports, making it accessible from any programming language.

A Simple Example¶

Here's MCP in action:

sequenceDiagram
    participant AI as AI Assistant
    participant MCP as MCP Client
    participant Server as MCP Server
    participant Tool as External Tool

    AI->>MCP: "Calculate 15% tip on $85"
    MCP->>Server: list_tools()
    Server-->>MCP: ["calculator", "currency_converter"]
    MCP->>Server: execute_tool("calculator", {"op": "multiply", "a": 85, "b": 0.15})
    Server->>Tool: Perform calculation
    Tool-->>Server: 12.75
    Server-->>MCP: {"result": 12.75}
    MCP-->>AI: "The tip is $12.75"

MCP vs Traditional Approaches¶

Real-World Applications¶

MCP enables scenarios like:

• AI Code Assistants accessing your local development environment
• Customer Service Bots querying multiple backend systems
• Data Analysis AIs connecting to various databases and data lakes
• Automation Agents controlling IoT devices and services

The Technical Foundation¶

MCP builds on established standards:

• JSON-RPC 2.0 for message format
• JSON Schema for parameter validation
• OpenRPC for service description
• Standard transports (stdio, HTTP, WebSocket)

This foundation ensures MCP is both powerful and familiar to developers.

Getting Started is Simple¶

# Install an MCP server
npm install -g @mcp/server-filesystem

# Run it
mcp-server-filesystem --path /your/data

# Connect from Python
from mcp import Client
client = Client("filesystem-server")
files = await client.list_resources()

Key Takeaways¶

• ✅ MCP standardizes how AI models interact with external tools
• ✅ It's language-agnostic and transport-flexible
• ✅ Built on proven standards like JSON-RPC 2.0
• ✅ Enables write-once, integrate-everywhere solutions
• ✅ Designed specifically for AI use cases

Ready to understand why this matters? Continue to Why MCP Matters →