How to Use the Moodle MCP in OpenAI Agents SDK

Q: How do I configure the Moodle MCP Server inside my OpenAI Agents SDK pipeline?

Install openai-agents and initialize MCPServerStreamableHttp with your Vinkius endpoint. Pass this server instance in the mcpservers list when instantiating your agent.

Q: Can an agent built with OpenAI Agents SDK send automated Moodle alerts?

Yes, the agent uses sendmessage to deliver instant messages to specific user IDs. It reads grades via getcoursegrades first, then decides whether to ping the student.

Q: Does the OpenAI Agents SDK support auto-discovery for this Moodle MCP Server?

Yes, it does. By setting cacheToolsList=True in your configuration, your agent automatically discovers all 10 tools, including getcourses and getcoursequizzes.

Q: How do I handle rate limits when pulling data from the LMS?

Configure the agent to cache responses from getcourseworkshops and getusers. This prevents your script from hitting API rate limits during heavy grading periods.

Run production-grade OpenAI Agents SDK workflows that directly query Moodle grades and message students when they fall behind.

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Connect Moodle MCP to OpenAI Agents SDK

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

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Academic status checks for OpenAI Agents SDK

Your agent calls `get_course_completion` to inspect student progress across active modules. It pulls live completion records directly from the LMS database and compares them against current syllabus milestones without manual export steps. This allows your OpenAI Agents SDK system to run scheduled audits on course participation via our MCP Server. If an agent detects incomplete modules, it triggers immediate follow-up routines to keep students on track.

Targeted grading analysis with this MCP Server

This MCP Server exposes `get_course_grades` so your agent can extract raw gradebook data for any enrolled student. It parses the numeric marks to pinpoint exactly who is struggling before the final exam week. You can build multi-agent handoffs where a grading supervisor agent identifies low scores and hands the task to a support agent. The support agent then uses `send_message` to ping the student with resources.

Class roster and group mapping

The agent uses `get_enrolled_users` and `get_course_groups` to map out the entire classroom structure. It identifies which students belong to which project teams and who is lagging in group submissions. You don't have to manually match spreadsheets anymore. Your OpenAI Agents SDK setup reads these groups in real time to coordinate team-specific announcements or workshop reminders.

Setup guide

Set up Moodle 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 Moodle tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Moodle 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="Moodle Agent",
            instructions="You have access to Moodle tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Moodle. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Moodle MCP in OpenAI Agents SDK

Install `openai-agents` and initialize `MCPServerStreamableHttp` with your Vinkius endpoint. Pass this server instance in the `mcp_servers` list when instantiating your agent.

Yes, the agent uses `send_message` to deliver instant messages to specific user IDs. It reads grades via `get_course_grades` first, then decides whether to ping the student.

Yes, it does. By setting `cacheToolsList=True` in your configuration, your agent automatically discovers all 10 tools, including `get_courses` and `get_course_quizzes`.

Configure the agent to cache responses from `get_course_workshops` and `get_users`. This prevents your script from hitting API rate limits during heavy grading periods.

All data transit runs through Vinkius's secure V8 sandbox, which isolates your API credentials. The agent only accesses specific grade parameters and message logs required for the immediate execution step, keeping institutional records safe.

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