How to Use the EdApp MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK agent update EdApp learner profiles?

Yes, it can. The agent uses listlearners to identify profiles and performs updates as needed. It handles the API handshake so you don't have to manage raw requests.

Connect your OpenAI Agents SDK to EdApp to automate learner management and track workforce training progress in production environments.

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Connect EdApp MCP to OpenAI Agents SDK

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

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Automate learner lifecycles

Your agent handles user onboarding and cleanup without manual intervention. By calling `create_new_learner` and `remove_learner`, the system ensures your training roster stays accurate as employees join or leave the company. This setup keeps your database clean. You define the logic once, and the agent executes the changes based on real-time triggers from your HR pipeline.

Real-time progress tracking

Monitor exactly how your team interacts with training modules through `get_course_progress_report`. Your agent can pull this data to identify which departments are falling behind on mandatory compliance training. It logs every interaction via `get_lesson_attempts_log`. You get granular visibility into engagement patterns, allowing you to adjust course content before completion rates drop.

Query training catalogs with OpenAI Agents SDK

Surface specific training materials by using `list_training_courses` and `list_course_collections`. Your agent sorts through thousands of microlearning units to suggest the right content for each user. It matches learner needs against the available catalog instantly. This removes the friction of manually searching through course lists to find relevant modules.

Setup guide

Set up EdApp 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 EdApp tools at runtime.
3

Create your Agent

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

Run the agent

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

You rely on the Vinkius zero-trust sandbox. Every request to the EdApp API requires an endpoint token, ensuring your agent only accesses what you explicitly permit.

Yes, it can. The agent uses `list_learners` to identify profiles and performs updates as needed. It handles the API handshake so you don't have to manage raw requests.

The server manages the connection, but you should implement retry logic in your agent code. This prevents request spikes that might trigger an EdApp API block.

It accesses PII like learner names and email addresses, along with progress logs and training statistics. All data stays ephemeral during the agent session.

You use the built-in guardrails in the SDK. These validate the tool output before it passes to the agent, preventing malformed data from affecting your workflow.

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