MCP 서버의 커뮤니티에 기여한 디렉토리는 학술 도서관, 연구 및 고등 교육과 관련되어 있습니다. 교육 도서관의 AI Practitioners에 의해 유지.

디렉토리와 가이드 내용 모두에 기여합니다. * 기여하는 방법에 대한 정보는 contributing.md.*

이용 및 속성: 당신은 포크에 오신 것을 환영합니다 및 당신의 특정한 청중/사용 케이스 또는 이 자원에 연결.

내용 표

• MCP란?
• 시작하기
• MCP Server 디렉토리
• 서버 Evaluate 방법

MCP는 무엇입니까?

Model Context Protocol (MCP)은 AI 애플리케이션과 외부 데이터 소스, 도구 (툴 호출) 및 시스템 간의 연결을 표준화 한 Anthropic에 의해 개발 된 오픈 소스 표준입니다.

왜 MCP Matters

MCP의 앞에, 외부 체계를 가진 연결 AI 신청은 주문 통합을 요구했습니다. 각 신청을 위해, 각 공구, 데이타베이스 및 서비스는 그것의 자신의 연결관을 필요로 할 것입니다, 개인적으로 건축되고 유지될 것입니다. MCP는 보편적인 표준과 같이 대체합니다: 일단 체계 또는 자료 근원은 MCP 서버, 어떤 MCP 호환이 되는 신청든지 직접 연결할 수 있습니다.

Academic MCP Guide and Directory

A community-contributed directory of MCP servers relevant to academic libraries, research, and higher education. Maintained by the AI Practitioners in Academic Libraries Community of Practice.

Contributions to both the directory and guide content are welcome. For information on how to contribute, see contributing.md.

Reuse and Attribution: You are welcome to fork and adapt to your specific audience/use case or link to this resource.

Table of Contents

• What is MCP?
• Getting Started
• Academic MCP Server Directory
• How to Evaluate Servers

What is MCP?

Model Context Protocol (MCP) is an open-source standard developed by Anthropic that standardized the connection between AI applications and external data sources, tools (tool calling), and systems.

Why MCP Matters

Before MCP, connecting AI applications with external systems required custom integrations. For each application, every tool, database, and service would need its own connector, which would have to be built and maintained individually. MCP replaces that with a universal standard: once a system or data source publishes an MCP server, any MCP-compatible application can connect to it directly. This dramatically lowers the engineering effort required on both sides. Developers, database providers, and publishers only need to implement the standard once, and their tools, publications, and datasets will immediately be accessible to any AI application. For application users, MCP facilitates access to a growing library of tools, data sources, and other services. For example, Claude’s desktop app support “connectors” that are built on MCP that let users link tools like Google Drive, Gmail, or other external services directly into a conversation.

Key Terms

MCP Server: a program that exposes data, tools, or capabilities to AI applications via the MCP standard. It acts as a bridge between the AI and an external system, such as a database or third-party service. It takes requests from MCP clients and returns relevant context or results.

MCP Client: the component within an AI application that initiates communication with MCP servers. It sends requests for data or tool use and processes the responses returned by the server.

Host: the AI application or environment in which the MCP client runs. Examples include Claude Desktop, an IDE plugin, or a custom AI assistant. The host manages the client’s connections to MCP servers.

MCP vs RAG

Another common approach to connecting AI with external information is Retrieval-Augmented Generation, or RAG. Both RAG and MCP helps maintain knowledge currency and allow AI applications or models to draw on outside sources rather than relying solely on what the model learned during training. They differ in how that outside information is managed.