Каталог MCP-серверов, релевантных академическим библиотекам, исследованиям и высшему образованию. Поддерживается практикующими ИИ в сообществе академических библиотек практики.

Приветствуются вклады как в каталог, так и в содержание руководства. *Для получения информации о том, как внести свой вклад, см.

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Таблица содержимого

• Что такое MCP?
• Начинаем. Академическая директория MCP Server Как оценить серверы

Что такое MCP?

Model Context Protocol (MCP) - это стандарт с открытым исходным кодом, разработанный Anthropic, который стандартизировал связь между приложениями ИИ и внешними источниками данных, инструментами (инструментальные вызовы) и системами.

###Почему MCP имеет значение До MCP подключение приложений ИИ к внешним системам требовало пользовательских интеграций. Для каждого приложения, каждого инструмента, базы данных и сервиса потребуется свой разъем, который должен быть построен и обслуживаться индивидуально. MCP заменяет это универсальным стандартом: как только система или источник данных публикует MCP-сервер, любое MCP-совместимое приложение может подключиться к нему напрямую.

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.