·작성자 AI Resource Hub Team
Model Context Protocol (MCP): AI 도구 통합 표준
MCP의 이해, AI 모델을 외부 도구와 데이터에 연결하는 방법, 그리고 이것이 왜 AI 통합의 보편적 표준이 되고 있는지.
What Is MCP?
The Model Context Protocol (MCP) is an open standard by Anthropic for connecting AI assistants to external data sources and tools. Think of it as USB-C for AI — one protocol that works everywhere.
Why MCP Matters
Before MCP, every AI integration was custom. Each tool needed its own plugin, API wrapper, and configuration. MCP standardizes this.
Architecture
- MCP Host: The AI application (e.g., Claude Desktop, Cursor).
- MCP Server: Exposes tools and resources via the protocol.
- MCP Client: Connects the host to servers.
Key Concepts
- Tools: Functions the AI can call (search, create file, query database).
- Resources: Data the AI can read (files, database records, API responses).
- Prompts: Reusable prompt templates shared between applications.
Building an MCP Server
1. Define your tools with JSON schemas.
2. Implement the MCP protocol (REST or stdio transport).
3. Register capabilities (tools, resources, prompts).
4. Test with MCP Inspector or Claude Desktop.
Ecosystem
- GitHub: MCP server for repositories and issues.
- PostgreSQL: MCP server for database queries.
- Slack: MCP server for messaging and channels.
- Filesystem: MCP server for local file access.
For Developers
- MCP SDKs available in TypeScript and Python.
- Follow the specification at modelcontextprotocol.io.
- Community servers growing rapidly — hundreds available.
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