google-classroom-automation

通过 Rube MCP 自动处理 Google Classroom 课程、作业与成员管理等教学事务。

来源

GitHub

更新于

2026-06-07

// 安全评估低风险

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正在进行安全审计…

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// 安装

复制安装指令，让 AI 自动完成配置 · 推荐新手

请帮我安装 askskill 上的 "google-classroom-automation" 技能：
1. 下载 https://raw.githubusercontent.com/ComposioHQ/awesome-claude-skills/master/composio-skills/google-classroom-automation/SKILL.md
2. 保存为 ~/.claude/skills/google-classroom-automation/SKILL.md
3. 装好后重载技能，告诉我可以用了

// 下载

下载 SKILL.md机读安装清单 ↗

// 用法示例

批量创建课程作业

输入

先搜索可用的 Google Classroom 工具及其最新参数 schema，然后为“高一数学”课程批量创建 5 次作业。每次作业包含标题、截止日期、100 分制分值和简短说明，并返回创建结果汇总。

预期产出

返回已创建作业的清单、对应截止日期、状态以及可能失败项的原因。

同步学生名单

输入

先搜索 Google Classroom 相关工具的当前 schema，然后将提供的邮箱名单同步到指定课程：已存在成员跳过，不在名单中的成员标记出来，并输出新增、跳过和异常结果。

预期产出

输出课程成员同步报告，包含新增学生、已存在学生、异常邮箱和差异项汇总。

汇总待批改作业

输入

先搜索 Google Classroom 工具及最新字段定义，再读取指定课程近两周的作业提交情况，筛选出待批改项目，并按作业名称统计未评分人数，生成简明汇总。

预期产出

得到一份待批改概览，列出每项作业的未评分人数、提交状态和处理优先级建议。

// 文档

Google Classroom Automation via Rube MCP

Automate Google Classroom operations through Composio's Google Classroom toolkit via Rube MCP.

Toolkit docs: composio.dev/toolkits/google_classroom

Prerequisites

Rube MCP must be connected (RUBE_SEARCH_TOOLS available)
Active Google Classroom connection via RUBE_MANAGE_CONNECTIONS with toolkit google_classroom
Always call RUBE_SEARCH_TOOLS first to get current tool schemas

Setup

Get Rube MCP: Add https://rube.app/mcp as an MCP server in your client configuration. No API keys needed — just add the endpoint and it works.

Verify Rube MCP is available by confirming RUBE_SEARCH_TOOLS responds
Call RUBE_MANAGE_CONNECTIONS with toolkit google_classroom
If connection is not ACTIVE, follow the returned auth link to complete setup
Confirm connection status shows ACTIVE before running any workflows

Tool Discovery

Always discover available tools before executing workflows:

RUBE_SEARCH_TOOLS
queries: [{use_case: "Google Classroom operations", known_fields: ""}]
session: {generate_id: true}

This returns available tool slugs, input schemas, recommended execution plans, and known pitfalls.

Core Workflow Pattern

Step 1: Discover Available Tools

RUBE_SEARCH_TOOLS
queries: [{use_case: "your specific Google Classroom task"}]
session: {id: "existing_session_id"}

Step 2: Check Connection

RUBE_MANAGE_CONNECTIONS
toolkits: ["google_classroom"]
session_id: "your_session_id"

Step 3: Execute Tools

RUBE_MULTI_EXECUTE_TOOL
tools: [{
  tool_slug: "TOOL_SLUG_FROM_SEARCH",
  arguments: {/* schema-compliant args from search results */}
}]
memory: {}
session_id: "your_session_id"

Known Pitfalls

Always search first: Tool schemas change. Never hardcode tool slugs or arguments without calling RUBE_SEARCH_TOOLS
Check connection: Verify RUBE_MANAGE_CONNECTIONS shows ACTIVE status before executing tools
Schema compliance: Use exact field names and types from the search results
Memory parameter: Always include memory in RUBE_MULTI_EXECUTE_TOOL calls, even if empty ({})
Session reuse: Reuse session IDs within a workflow. Generate new ones for new workflows
Pagination: Check responses for pagination tokens and continue fetching until complete

Quick Reference

Operation	Approach
Find tools	`RUBE_SEARCH_TOOLS` with Google Classroom-specific use case
Connect	`RUBE_MANAGE_CONNECTIONS` with toolkit `google_classroom`
Execute	`RUBE_MULTI_EXECUTE_TOOL` with discovered tool slugs
Bulk ops	`RUBE_REMOTE_WORKBENCH` with `run_composio_tool()`
Full schema	`RUBE_GET_TOOL_SCHEMAS` for tools with `schemaRef`