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LearnMCP XAPI MCP Server

@DavidLMS

代码仓库主页

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

DavidLMS

关于 LearnMCP XAPI MCP Server

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#education#ai#learning

LearnMCP XAPI MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

LearnMCP XAPI MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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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