How to Use the LearnWorlds MCP in OpenAI Agents SDK

Q: How do I connect my LearnWorlds school to the OpenAI Agents SDK via MCP?

Install openai-agents, then instantiate MCPServerStreamableHttp with your Vinkius server URL. Pass this instance into your Agent constructor using the mcpservers parameter to auto-discover all eight LMS tools.

Q: Can the OpenAI Agents SDK handle bulk enrollments in LearnWorlds?

Yes. Your agent loops through your target user list, calls createstudent for new profiles, and then runs enrollstudent for each account. Set cacheToolsList=True in your SDK config to maintain high performance during bulk operations.

Build secure, production-grade agent workflows that manage courses and students on LearnWorlds using the OpenAI Agents SDK.

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Connect LearnWorlds MCP to OpenAI Agents SDK

Create your Vinkius account to connect LearnWorlds 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 LearnWorlds with OpenAI Agents SDK

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Secure student provisioning with OpenAI Agents SDK

The `create_student` tool provisions new accounts directly inside your school database without exposing raw credentials. This MCP Server exposes this action so your OpenAI Agents SDK workflow can register users the second they sign up. The agent validates input parameters before triggering the creation call, ensuring no bad email formats hit your database. Handoffs between specialized agents let you route the newly created student profile to a billing specialist agent. This second agent uses `enroll_student` to bind the user to their purchased course. You get a clean execution trace in your OpenAI dashboard for every single enrollment.

Automated subscription tracking via agent handoffs

The `list_subscriptions` tool pulls active student plans to verify who should retain access to your premium course tiers. By connecting this tool to your OpenAI Agents SDK setup, you build autonomous billing agents that check membership status on a schedule. If an agent detects an expired subscription, it triggers actions to restrict access. This workflow runs safely because the SDK uses strict guardrails to validate agent decisions before they execute. Your agent checks payment history using `list_payments` before modifying any student access, preventing accidental account suspensions.

Course curriculum auditing using this MCP Server

The `get_course_contents` tool exposes the entire syllabus structure of your active courses directly to your LLM. When you connect this MCP Server to your OpenAI Agents SDK, your agent can analyze course structures and suggest logical updates based on student feedback. It reads the modules, quizzes, and video details instantly. You do not write custom parser code to read the LMS structure. The agent auto-discovers the tool schema via this MCP, allowing it to cross-reference course outlines with student details fetched from `get_student_details`.

Setup guide

Set up LearnWorlds 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 LearnWorlds tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate LearnWorlds 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="LearnWorlds Agent",
            instructions="You have access to LearnWorlds 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 LearnWorlds MCP in OpenAI Agents SDK

Install `openai-agents`, then instantiate `MCPServerStreamableHttp` with your Vinkius server URL. Pass this instance into your Agent constructor using the `mcp_servers` parameter to auto-discover all eight LMS tools.

Yes. Your agent loops through your target user list, calls `create_student` for new profiles, and then runs `enroll_student` for each account. Set `cacheToolsList=True` in your SDK config to maintain high performance during bulk operations.

You trace every tool invocation directly inside your OpenAI developer dashboard. If a call to `list_payments` or `get_student_details` fails, the dashboard shows the exact payload sent by the agent and the raw error response from Vinkius.

No. The SDK automatically discovers the schema for all tools, including `list_students` and `list_courses`, directly from the Vinkius MCP Server endpoint, meaning you write zero boilerplate code.

The server processes student email addresses, payment histories, and subscription statuses inside isolated V8 sandboxes. No credentials or user records are stored on Vinkius, and the OpenAI Agents SDK connects via a single-token HTTPS stream.

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