MCP(Model Context Protocol) 완전 가이드: AI 에이전트를 도구와 연결하는 방법

[AI 앱 (Host)]
     |
     v
[MCP Client] ←→ [MCP Server] ←→ [실제 도구/데이터]

{
  "name": "search_database",
  "description": "제품 데이터베이스에서 검색합니다",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": {"type": "string"},
      "limit": {"type": "number"}
    }
  }
}

from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
import psycopg2

app = Server("my-database-server")

@app.list_tools()
async def list_tools() -> list[Tool]:
    return [
        Tool(
            name="query_products",
            description="제품 DB에서 검색",
            inputSchema={
                "type": "object",
                "properties": {
                    "keyword": {"type": "string", "description": "검색 키워드"}
                },
                "required": ["keyword"]
            }
        )
    ]

@app.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
    if name == "query_products":
        conn = psycopg2.connect("postgresql://...")
        cur = conn.cursor()
        cur.execute(
            "SELECT name, price FROM products WHERE name ILIKE %s LIMIT 5",
            (f"%{arguments['keyword']}%",)
        )
        results = cur.fetchall()
        return [TextContent(
            type="text",
            text=str(results)
        )]

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

{
  "mcpServers": {
    "my-database": {
      "command": "python3",
      "args": ["/path/to/my_mcp_server.py"],
      "env": {
        "DATABASE_URL": "postgresql://..."
      }
    }
  }
}