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Google Classroom MCP Server: Automate Workflows, Boost Collaboration - MCP Implementation

Google Classroom MCP Server: Automate Workflows, Boost Collaboration

Google Classroom MCP Server: Seamlessly integrate, automate workflows, and boost collaboration. Secure, scalable, educator-approved classroom management made effortless!

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This tool saved users approximately 9260 hours last month!

About Google Classroom MCP Server

What is Google Classroom MCP Server: Automate Workflows, Boost Collaboration?

At its core, the Google Classroom MCP Server acts as a bridge between AI assistants like Claude and Google Classroom ecosystems. This Model Context Protocol (MCP)-enabled tool empowers users to access course data, assignments, and announcements programmatically, streamlining workflows and fostering seamless collaboration. By leveraging OAuth 2.0 authentication and the Classroom API, it delivers a secure, context-aware interface for automated tasks that would otherwise require manual navigation.

How to use Google Classroom MCP Server: Automate Workflows, Boost Collaboration?

Implementation follows a three-step cadence: configuration (setting up API access and environment variables), integration (linking the server to compatible AI platforms), and execution (issuing natural language queries to retrieve or manipulate data). Users can query assignment deadlines, generate course overviews, or trigger automated notifications without leaving their primary workspace.

Google Classroom MCP Server Features

Key Features of Google Classroom MCP Server

  • Granular Permissions Management: Role-based access controls ensure only authorized users interact with sensitive academic data.
  • Real-Time Data Synchronization: Instant updates to assignments, grades, and calendar events via push notifications.
  • Multilingual Support: Context-aware language processing for international classrooms.
  • Workflow Automation: Pre-built templates for recurring tasks like grade aggregation or attendance tracking.

Use Cases for Google Classroom MCP Server

Academic professionals benefit from scenarios like:
– Professors auto-generating syllabus summaries for new students
– TAs programmatically flagging overdue submissions
– Cross-departmental teams synchronizing curriculum updates

Google Classroom MCP Server FAQ

FAQ: Google Classroom MCP Server

  • Q: How does it handle API rate limits?
    The server implements exponential backoff algorithms and caching mechanisms to ensure reliability during high-traffic periods.
  • Q: Is it compatible with self-hosted LMS platforms?
    While designed for Google Classroom, the MCP architecture allows extension to other systems via API connectors.
  • Q: What security measures are in place?
    End-to-end encryption, periodic audit logs, and 2FA authentication for administrative access provide robust protection.

Content

Google Classroom MCP Server

An MCP (Model Context Protocol) server that provides access to Google Classroom data through Claude and other AI assistants that support the MCP protocol.

Setup

Prerequisites

  • Node.js (v16 or higher)
  • A Google Cloud Platform project with the Google Classroom API enabled
  • OAuth 2.0 client credentials for the Google Classroom API

Installation

  1. Clone this repository
  2. Install dependencies:
npm install
  1. Place your Google OAuth client credentials in a file named credentials.json in the project root:
{
  "web": {
    "client_id": "YOUR_CLIENT_ID",
    "project_id": "YOUR_PROJECT_ID",
    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
    "token_uri": "https://oauth2.googleapis.com/token",
    "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
    "client_secret": "YOUR_CLIENT_SECRET",
    "redirect_uris": ["http://localhost:3000/auth/google/callback"]
  }
}
  1. Authenticate with Google:
node index.js auth

This will launch a browser window to complete the OAuth flow and save your credentials to tokens.json.

  1. Configure Claude to use this server by updating claude_desktop_config.json (typically in %APPDATA%\Claude\):
{
  "mcpServers": {
    "class": {
      "command": "node",
      "args": [
        "PATH_TO_YOUR_DIRECTORY\\index.js"
      ]
    }
  }
}

Usage

Available Tools

The server provides several tools for interacting with Google Classroom:

1. courses - List all your Google Classroom courses

Use the 'courses' tool to get a list of all your Google Classroom courses

2. course-details - Get detailed information about a specific course

Use the 'course-details' tool with the courseId parameter to get details and announcements for a specific course

Parameters:

  • courseId: The ID of the course (can be obtained from the courses tool)

3. assignments - Get assignments for a specific course

Use the 'assignments' tool with the courseId parameter to get assignments and your submissions for a specific course

Parameters:

  • courseId: The ID of the course (can be obtained from the courses tool)

Example Prompts for Claude

  1. Show me all my Google Classroom courses
  2. Get details for my Math course with ID 123456789
  3. Show me all assignments for my History course with ID 987654321

Permissions

The server requests the following Google Classroom API permissions:

  • classroom.courses.readonly - To access course information
  • classroom.announcements.readonly - To access course announcements
  • classroom.coursework.me.readonly - To access your coursework and assignments
  • classroom.rosters.readonly - To access class rosters

Troubleshooting

If you encounter permission errors, try:

  1. Running the auth command again to refresh permissions:

    node index.js auth

  2. Ensuring your Google account is added as a test user in the Google Cloud Console if your app is in testing mode

  3. Checking the OAuth scopes in the authenticateAndSaveCredentials function to ensure they match your needs

Notes

  • This server is designed to be used with Claude AI or other MCP-compatible assistants
  • All API requests are made using your authenticated Google account
  • Token refresh is handled automatically by the server
  • Sensitive credentials are stored locally in the tokens.json file

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