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LearnMCP XAPI MCP Server

@DavidLMS

基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

DavidLMS

LearnMCP XAPI MCP Server について

An open-source MCP (Model Context Protocol) server that enables AI agents to record and retrieve learning activities through xAPI-compliant Learning Record Stores

LearnMCP XAPI MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "learnmcp-xapi": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "learnmcp-env"
      ]
    }
  }
}

LearnMCP XAPI 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 LearnMCP-xAPI?

LearnMCP-xAPI is an open-source MCP (Model Context Protocol) server that enables AI agents to record and retrieve learning activities through xAPI-compliant Learning Record Stores. It creates a bridge between AI interactions and learning analytics, allowing intelligent systems to build contextual understanding of what learners know, practice, and achieve. It is designed for developers building AI tutoring systems, personalized learning assistants, or educational analytics platforms.

How to use LearnMCP-xAPI?

Install by cloning the repository and installing dependencies (Python 3.8+ required). Configure by copying .env.example to .env and setting your LRS plugin (e.g., lrsql, ralph, veracity), student identifier (ACTOR_UUID), and LRS credentials. Then use with an MCP-compatible client like Claude Desktop. The server exposes three core tools via MCP: statement recording, progress retrieval, and activity vocabulary management.

Key features of LearnMCP-xAPI

  • xAPI 1.0.3 compliance for interoperability with educational ecosystems.
  • Native MCP integration for seamless AI agent connectivity.
  • Plugin architecture supporting multiple Learning Record Stores (LRS SQL, Ralph LRS, Veracity Learning).
  • Privacy-preserving design using unique student identifiers (ACTOR_UUID).
  • Intelligent statement generation from natural language learning activities.
  • Contextual learning queries for adaptive AI responses.
  • Robust retry logic and error

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