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학습 보조 어시스턴트 MCP Server

@ktwome

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

ktwome

학습 보조 어시스턴트 MCP Server について

학습 보조 웹 서비스

학습 보조 어시스턴트 MCP Server の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

학습 보조 어시스턴트 MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is 학습 보조 어시스턴트?

학습 보조 어시스턴트 is a PDF document analysis service that helps users learn. It converts PDFs to Markdown, performs RAG (Retrieval-Augmented Generation) on user questions, and generates practice problems by difficulty level. The server is built with FastAPI and Vue, and uses the EXAONE language model for content organization.

How to use 학습 보조 어시스턴트?

The server provides a web interface built with Vue and Vuetify. Users upload PDF files via the frontend, which are then converted to Markdown. Converted files are saved on the server and can be viewed in a history list. Planned features include core concept extraction, problem generation, chat, and RAG. No specific installation or configuration commands are provided in the README.

Key features of 학습 보조 어시스턴트

  • PDF upload and conversion to Markdown
  • RAG-based question answering (planned)
  • Problem generation by difficulty (planned)
  • EXAONE-assisted content organization
  • Saved conversion history and display
  • Core concept extraction (planned)

Use cases of 학습 보조 어시스턴트

  • Students uploading textbook PDFs for summarized Markdown notes
  • Self-study with automatically generated practice questions
  • Extracting key concepts from academic papers
  • Quick review of large PDF documents via structured Markdown

よくある質問

1

What file formats are supported?

The server only supports PDF files for input.

2

Is this a web-based service?

Yes, it uses a FastAPI backend and a Vue frontend, accessible via a web browser.

3

What LLM is used for content organization?

The EXAONE model is used to organize content from each PDF page.

4

Are all listed features currently implemented?

Some features (RAG, problem generation, core concept extraction, chat) are listed as planned in the development history and may not be fully functional yet.

5

How are converted files stored?

Converted Markdown files are saved on the server and can be viewed in a list showing the PDF title and conversion time.

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