MCP (Model Context Protocol) Complete Guide

What is MCP?

MCP (Model Context Protocol) is an open-source protocol Anthropic published in late 2024. It lets AI models communicate with external resources — file systems, databases, web APIs, dev tools — in a standardized way.

Client (Claude Desktop, Cursor, VS Code)
    ↕ MCP Protocol
Server (file system, DB, web API, Git, Slack...)

Why MCP?

Before MCP, every AI app had to write its own integration code for every external tool. MCP provides a USB-like standard interface: write an MCP server once and reuse it across all compatible clients.

Before MCP	With MCP
Each app writes its own API integration	Write MCP server once → all clients use it
Model changes require code rewrites	Works regardless of model or client
Security/auth reimplemented repeatedly	Handled consistently on the server side

---

MCP Architecture

┌─────────────────────────────────────────┐
│             MCP Client                  │
│  (Claude Desktop / Cursor / VS Code)    │
└────────────────┬────────────────────────┘
                 │ JSON-RPC over stdio / SSE
┌────────────────▼────────────────────────┐
│              MCP Server                 │
│  ┌──────────┐  ┌──────────┐  ┌───────┐ │
│  │  Tools   │  │Resources │  │Prompts│ │
│  └──────────┘  └──────────┘  └───────┘ │
└────────────────┬────────────────────────┘
                 │
     ┌───────────┼───────────┐
     ▼           ▼           ▼
  File system  Database   Web API

3 Core Concepts

• Tools: Functions the AI can call (similar to Function Calling)
• Resources: Data the AI can read (files, DB records, etc.)
• Prompts: Reusable prompt templates

---

Quick Start: Python MCP Server

Installation

pip install mcp
# or using uv (recommended)
pip install uv
uv add mcp

Your First MCP Server

The list_tools function is implemented below. It handles the core logic described above:

# server.py
from mcp.server import Server
from mcp.server.models import InitializationOptions
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
import mcp.types as types

# Create server instance
server = Server("my-first-server")

@server.list_tools()
async def list_tools() -> list[Tool]:
    """Return list of available tools"""
    return [
        Tool(
            name="calculate",
            description="Evaluates a mathematical expression",
            inputSchema={
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "Expression to evaluate (e.g. 2 + 3 * 4)"
                    }
                },
                "required": ["expression"]
            }
        ),
        Tool(
            name="get_current_time",
            description="Returns the current time",
            inputSchema={
                "type": "object",
                "properties": {}
            }
        )
    ]

@server.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
    """Handle tool calls"""
    if name == "calculate":
        expression = arguments.get("expression", "")
        try:
            # Safe eval (use a proper sandbox in production)
            result = eval(expression, {"__builtins__": {}}, {})
            return [TextContent(type="text", text=f"{expression} = {result}")]
        except Exception as e:
            return [TextContent(type="text", text=f"Error: {str(e)}")]
    
    elif name == "get_current_time":
        from datetime import datetime
        now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        return [TextContent(type="text", text=f"Current time: {now}")]
    
    raise ValueError(f"Unknown tool: {name}")

async def main():
    # Run in stdio mode (for local Claude Desktop connections)
    async with stdio_server() as (read_stream, write_stream):
        await server.run(
            read_stream,
            write_stream,
            InitializationOptions(
                server_name="my-first-server",
                server_version="1.0.0",
                capabilities=server.get_capabilities(
                    notification_options=None,
                    experimental_capabilities={}
                )
            )
        )

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

---

Real-World Example: File System MCP Server

The list_tools function is implemented below. It handles the core logic described above:

# file_server.py
from mcp.server import Server
from mcp.server.models import InitializationOptions
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
from pathlib import Path
import os

server = Server("file-system-server")
ALLOWED_DIR = Path(os.environ.get("ALLOWED_DIR", "/tmp/mcp-files"))
ALLOWED_DIR.mkdir(exist_ok=True)

@server.list_tools()
async def list_tools() -> list[Tool]:
    return [
        Tool(
            name="read_file",
            description="Read file contents",
            inputSchema={
                "type": "object",
                "properties": {
                    "path": {"type": "string", "description": "File path"}
                },
                "required": ["path"]
            }
        ),
        Tool(
            name="write_file",
            description="Write content to a file",
            inputSchema={
                "type": "object",
                "properties": {
                    "path": {"type": "string", "description": "File path"},
                    "content": {"type": "string", "description": "Content to write"}
                },
                "required": ["path", "content"]
            }
        ),
        Tool(
            name="list_files",
            description="List files in a directory",
            inputSchema={
                "type": "object",
                "properties": {
                    "directory": {"type": "string", "description": "Directory path (defaults to base dir)"}
                }
            }
        )
    ]

def validate_path(path_str: str) -> Path:
    """Security: only allow paths within the permitted directory"""
    path = (ALLOWED_DIR / path_str).resolve()
    if not str(path).startswith(str(ALLOWED_DIR)):
        raise ValueError("Access denied: path is outside the allowed directory")
    return path

@server.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
    if name == "read_file":
        path = validate_path(arguments["path"])
        if not path.exists():
            return [TextContent(type="text", text=f"Error: file not found: {path}")]
        content = path.read_text(encoding="utf-8")
        return [TextContent(type="text", text=content)]
    
    elif name == "write_file":
        path = validate_path(arguments["path"])
        path.parent.mkdir(parents=True, exist_ok=True)
        path.write_text(arguments["content"], encoding="utf-8")
        return [TextContent(type="text", text=f"File saved: {path.name}")]
    
    elif name == "list_files":
        dir_path = validate_path(arguments.get("directory", "."))
        if not dir_path.is_dir():
            return [TextContent(type="text", text="Not a directory")]
        
        files = []
        for item in sorted(dir_path.iterdir()):
            if item.is_dir():
                files.append(f"📁 {item.name}/")
            else:
                size = item.stat().st_size
                files.append(f"📄 {item.name} ({size:,} bytes)")
        
        result = "\n".join(files) if files else "No files found"
        return [TextContent(type="text", text=result)]
    
    raise ValueError(f"Unknown tool: {name}")

async def main():
    async with stdio_server() as (read_stream, write_stream):
        await server.run(read_stream, write_stream,
            InitializationOptions(server_name="file-system-server", server_version="1.0.0",
                capabilities=server.get_capabilities(notification_options=None, experimental_capabilities={})))

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

---

Building an MCP Server with TypeScript

npm install @modelcontextprotocol/sdk

Import the required modules and set up the dependencies:

// server.ts
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import {
  CallToolRequestSchema,
  ListToolsRequestSchema,
} from "@modelcontextprotocol/sdk/types.js";
import fetch from "node-fetch";

const server = new Server(
  { name: "weather-server", version: "1.0.0" },
  { capabilities: { tools: {} } }
);

// Define tools
server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [
    {
      name: "get_weather",
      description: 'Get current weather for a city. MCP (Model Context Protocol) Complete Guide에 대한 완전한 가이드입니다. 실전 예제와 함께 핵심 개념부터 고급 활용까지 다룹니다.',
      inputSchema: {
        type: "object",
        properties: {
          city: {
            type: "string",
            description: "City name (in English)",
          },
        },
        required: ["city"],
      },
    },
  ],
}));

// Handle tool calls
server.setRequestHandler(CallToolRequestSchema, async (request) => {
  const { name, arguments: args } = request.params;

  if (name === "get_weather") {
    const city = args?.city as string;
    const apiKey = process.env.OPENWEATHER_API_KEY;

    const response = await fetch(
      `https://api.openweathermap.org/data/2.5/weather?q=${city}&appid=${apiKey}&units=metric`
    );
    const data = (await response.json()) as any;

    if (data.cod !== 200) {
      return {
        content: [{ type: "text", text: `Error: ${data.message}` }],
      };
    }

    return {
      content: [
        {
          type: "text",
          text: `${city} weather: ${data.weather[0].description}, ${data.main.temp}°C (feels like ${data.main.feels_like}°C)`,
        },
      ],
    };
  }

  throw new Error(`Unknown tool: ${name}`);
});

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

---

Connecting to Claude Desktop

Configuration file:

// ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
// %APPDATA%\Claude\claude_desktop_config.json (Windows)
{
  "mcpServers": {
    "my-calculator": {
      "command": "python",
      "args": ["/path/to/server.py"],
      "env": {}
    },
    "file-system": {
      "command": "python",
      "args": ["/path/to/file_server.py"],
      "env": {
        "ALLOWED_DIR": "/Users/username/Documents"
      }
    },
    "weather": {
      "command": "node",
      "args": ["/path/to/weather-server/build/index.js"],
      "env": {
        "OPENWEATHER_API_KEY": "your-api-key-here"
      }
    }
  }
}

Restart Claude Desktop and a 🔧 tool icon will appear at the bottom of the chat window.

---

Using Resources and Prompts

Resources (Reading Data)

The list_resources function is implemented below. It handles the core logic described above:

from mcp.types import Resource, TextResourceContents

@server.list_resources()
async def list_resources() -> list[Resource]:
    return [
        Resource(
            uri="file:///config.json",
            name="Config file",
            description="Application configuration",
            mimeType="application/json"
        )
    ]

@server.read_resource()
async def read_resource(uri: str) -> str | bytes:
    if uri == "file:///config.json":
        return '{"version": "1.0", "debug": false}'
    raise ValueError(f"Unknown resource: {uri}")

Prompts (Reusable Templates)

The list_prompts function is implemented below. It handles the core logic described above:

from mcp.types import Prompt, PromptArgument, GetPromptResult, PromptMessage

@server.list_prompts()
async def list_prompts() -> list[Prompt]:
    return [
        Prompt(
            name="code_review",
            description="Code review request prompt",
            arguments=[
                PromptArgument(name="language", description="Programming language", required=True),
                PromptArgument(name="focus", description="Review focus area", required=False)
            ]
        )
    ]

@server.get_prompt()
async def get_prompt(name: str, arguments: dict | None) -> GetPromptResult:
    if name == "code_review":
        language = arguments.get("language", "code") if arguments else "code"
        focus = arguments.get("focus", "overall quality") if arguments else "overall quality"
        
        return GetPromptResult(
            description=f"{language} code review",
            messages=[
                PromptMessage(
                    role="user",
                    content=TextContent(
                        type="text",
                        text=f"""Please review the following {language} code with a focus on {focus}:
                        
1. Code quality and readability
2. Potential bugs and errors
3. Performance optimization opportunities
4. Security vulnerabilities
5. Improvement suggestions (with code examples)"""
                    )
                )
            ]
        )
    raise ValueError(f"Unknown prompt: {name}")

---

Popular MCP Servers

Official MCP servers from Anthropic and the community:

Run the following commands:

# Filesystem server (official)
npx -y @modelcontextprotocol/server-filesystem /path/to/directory

# SQLite server (official)
npx -y @modelcontextprotocol/server-sqlite /path/to/database.db

# GitHub server (official)
npx -y @modelcontextprotocol/server-github

# PostgreSQL server
npx -y @modelcontextprotocol/server-postgres postgresql://user:pass@localhost/db

# Slack server
npx -y @modelcontextprotocol/server-slack

# Brave Search server
npx -y @modelcontextprotocol/server-brave-search

---

Conclusion

MCP is rapidly becoming core infrastructure for the AI agent ecosystem. Learning it now gives you a significant edge in AI agent development.

Next steps:

• Claude API Complete Guide
• LangChain Complete Guide
• RAG Implementation Guide

---

Frequently Asked Questions (FAQ)

Q. When would I use this in practice?

A. MCP (Model Context Protocol) is Anthropic’s open standard for connecting AI to external tools. Learn how to build and co…

Q. What should I read before this?

A. Follow the previous article or related articles links at the bottom of each post to learn in sequence.

Q. Where can I study this more deeply?

A. Check cppreference and the relevant library’s official documentation. The reference links at the end of the article are also worth using.

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Related Articles (Internal Links)

Other articles related to this topic.

• FastAPI Complete Guide | Python REST API· Async
• gRPC Complete Guide | Protocol Buffers· Service Definitions
• Claude API Complete Guide | Anthropic SDK Usage

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Keywords Covered in This Article (Related Search Terms)

This article covers MCP, Claude, AI, Agent, Python, TypeScript, Tools, Anthropic.