How to Use the Classe365 MCP in OpenAI Agents SDK

Build production-grade student management agents with OpenAI Agents SDK. Connect your MCP Server securely.

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Connect Classe365 MCP to OpenAI Agents SDK

Create your Vinkius account to connect Classe365 to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Classe365 with OpenAI Agents SDK

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Automate student onboarding with OpenAI Agents SDK

Your admission agent needs to register new enrollments without hallucinating data fields. Connecting this MCP Server gives your agent direct write access to your institution's database. When a new application clears review, the system calls `create_student_profile` to generate the official record. Built-in OpenAI guardrails validate the payload before execution, ensuring bad data never hits your system.

Query academic records across departments

Agents handling student support require immediate access to course schedules and grades. Instead of building custom API wrappers, pass this MCP server to your agent constructor. The AI auto-discovers `list_academic_records` and `list_exam_assessments`. It pulls department sections and assessment scores directly into the context window, allowing specialized agents to answer complex advising questions.

Track daily attendance patterns natively

Monitoring student participation usually involves exporting CSVs and running manual reports. Your production agent changes that entirely. Using `list_student_attendance` alongside `list_school_classes`, the model analyzes absence trends across specific courses. You get full visibility into these operations through the OpenAI tracing dashboard.

Setup guide

Set up Classe365 MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Classe365 tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Classe365 tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Classe365 tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Classe365 Agent",
            instructions="You have access to Classe365 tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Classe365 MCP in OpenAI Agents SDK

Initialize an MCPServerStreamableHttp object with your Vinkius endpoint. Pass it inside the mcp_servers array when building your Agent instance. Set cacheToolsList=True to speed up tool discovery.

Yes, the agent automatically exposes read functions to your model. It can execute queries against student grades and class configurations without manual prompt engineering.

If a lookup fails, you can configure a specialized handoff agent to take over. The system will either prompt a human operator or attempt an alternative search strategy.

The integration provides full directory access. Your application can pull the entire roster using the list capabilities and process the results in batches.

Exam scores and attendance histories stay inside an ephemeral V8 Isolate Sandbox. Vinkius destroys the environment immediately after your OpenAI agent finishes executing the tool call.

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