How do I get a Google Classroom access token?

Go to [Google Cloud Console](https://console.cloud.google.com/), enable the Google Classroom API, create OAuth 2.0 credentials, and generate an access token with scopes: `classroom.courses`, `classroom.rosters`, `classroom.profile`. Use Google's OAuth Playground for quick testing.

Can I create and grade assignments?

Yes! Use `create_course_work` to create assignments with due dates and max points. Use `list_submissions` to see who turned in work, then `get_submission` to review individual student work. After grading, use `return_submission` to send it back to the student.

Can I manage student enrollment?

This MCP focuses on read-only operations for safety: listing students/teachers, viewing assignments, and tracking submissions. To add/remove students, use the Google Classroom web interface directly.

Google Classroom MCP Server for Pydantic AI 14 tools — connect in under 2 minutes

Name: Google Classroom
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 14 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Google Classroom through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Google Classroom "
            "(14 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Google Classroom?"
    )
    print(result.data)

asyncio.run(main())

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About Google Classroom MCP Server

Connect your Google Classroom to any AI agent and streamline your teaching workflows through natural conversation. Manage courses, create assignments, track student submissions, and grade work — all via AI commands.

Pydantic AI validates every Google Classroom tool response against typed schemas, catching data inconsistencies at build time. Connect 14 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Course Management — List, create, and update courses with full details
Roster Tracking — List enrolled students and assigned teachers for any course
Assignments — Create coursework, set due dates, and track all assignments
Submission Monitoring — Check who turned in work, who's late, and who hasn't submitted
Grading — Review individual submissions, return graded work to students
Announcements — Post important updates to the course stream

The Google Classroom MCP Server exposes 14 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Google Classroom to Pydantic AI via MCP

Follow these steps to integrate the Google Classroom MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 14 tools from Google Classroom with type-safe schemas

Why Use Pydantic AI with the Google Classroom MCP Server

Pydantic AI provides unique advantages when paired with Google Classroom through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Google Classroom integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Google Classroom connection logic from agent behavior for testable, maintainable code

Google Classroom + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Google Classroom MCP Server delivers measurable value.

Type-safe data pipelines: query Google Classroom with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Google Classroom tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Google Classroom and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Google Classroom responses and write comprehensive agent tests

Google Classroom MCP Tools for Pydantic AI (14)

These 14 tools become available when you connect Google Classroom to Pydantic AI via MCP:

create_announcement

Announcements appear in the course stream and can be used for important updates, reminders, or general communication. Post a new announcement to a Google Classroom course

create_course

Returns the new course ID for future reference. Only users with appropriate permissions can create courses. Create a new Google Classroom course

create_course_work

The assignment will be visible to all students in the course. Supports assignments, quizzes, and materials types. Create a new assignment or coursework in Google Classroom

get_course

Use the course ID obtained from list_courses. Get details of a specific Google Classroom course

get_submission

Get a specific student's submission details

list_announcements

Use this to check recent course announcements and important updates. List all announcements in a Google Classroom course

list_course_work

Returns assignment title, due date, state (DRAFT/PUBLISHED), max points, and creation time. Use this to track all assignments and their deadlines. List all assignments and coursework for a Google Classroom course

list_courses

Essential first step to identify which course to work with before querying students, assignments, or submissions. List all Google Classroom courses

list_students

Use this to check enrollment, identify students for grading, or verify class roster. List all students enrolled in a Google Classroom course

list_submissions

Returns student ID, submission state (NEW/CREATED/TURNED_IN/RETURNED), grade, and late status. List student submissions for a specific assignment

list_teachers

List all teachers of a Google Classroom course

return_submission

This is typically done after the teacher has reviewed and graded the work. The student will be notified that their work has been returned. Return a graded assignment to the student

turn_in_submission

The student must have created the submission first. This is equivalent to clicking "Turn In" in the Classroom UI. Turn in a student's assignment submission

update_course

Requires the course ID and at least one field to update. Update an existing Google Classroom course

Example Prompts for Google Classroom in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Google Classroom immediately.

"List all my courses and show students in Math 101."

"Create an assignment called 'Chapter 5 Problems' due 2024-05-15 for course 12345, max 100 points."

"Show me who turned in the Chapter 5 assignment."

Troubleshooting Google Classroom MCP Server with Pydantic AI

Common issues when connecting Google Classroom to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Google Classroom + Pydantic AI FAQ

Common questions about integrating Google Classroom MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Google Classroom MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.