学術ライブラリ、研究、高等教育に関連するMCPサーバーのコミュニティ・コントリビュートされたディレクトリ。 アカデミック・ライブラリー・コミュニティ・オブ・プラクティスにおけるAI実践者による維持

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コンテンツの表

• MCPとは?
• 始める
• 学術MCPサーバーディレクトリ
• サーバーを評価する方法

MCPとは?

モデルコンテクストプロトコル(MCP)は、AIアプリケーションと外部データソース、ツール(ツール)、システム間の接続を標準化したAnthropicによって開発されたオープンソース標準です。

なぜ MCP のマター

MCP 以前は、AI アプリケーションを外部システムに接続し、カスタム統合が必要です。 各アプリケーションでは、すべてのツール、データベース、およびサービスが独自のコネクタを必要とし、個別に構築および維持する必要があります。 MCP は、システムやデータソースが MCP サーバーを公開すると、MCP 互換のアプリケーションが直接接続できるように置き換えます.

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.