How to Use the UKG Pro Learning MCP in OpenAI Agents SDK
Build production systems with OpenAI Agents SDK and UKG Pro Learning.
Works with every AI agent you already use
…and any MCP-compatible client
Connect UKG Pro Learning MCP to OpenAI Agents SDK
Create your Vinkius account to connect UKG Pro Learning 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.
Manage User Details with the MCP Server
Need user info? Use `users` to grab specific employee details by ID. This gives your agent a clean profile, letting it know who's doing what across UKG Pro Learning. Agents can access names and identifiers instantly. You don't have to guess; the tool delivers structured data right when you need it.
Track Training Paths Using OpenAI Agents SDK
The `curricula` tool lists all active learning paths for an employee. Your agent uses this to see what training structure a user belongs to within UKG Pro Learning. Checking these paths lets your client understand the full scope of required development, moving beyond just checking individual courses.
Check Current Enrollments on the MCP Server
To see what an employee is currently signed up for, call `enrollments`. This tool lists all active records tied to a specific user. It's how your agent verifies participation status within UKG Pro Learning. It lets you determine if the user needs to take action or if they’re already enrolled in that module.
Set up UKG Pro Learning MCP in OpenAI Agents SDK
Prerequisites
- Python 3.10+ installed
-
openai-agentspackage (pip install openai-agents) - Active Vinkius subscription with a valid endpoint token
- 1
Install the SDK
Run
pip install openai-agentsto install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed. - 2
Connect via SSE transport
Use
MCPServerSsewith your Vinkius endpoint URL. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. The SDK auto-discovers all UKG Pro Learning tools at runtime. - 3
Create your Agent
Pass the MCP to
Agent(mcp_servers=[server]). The agent receives UKG Pro Learning tools as native definitions — JSON schemas resolve automatically. - 4
Run the agent
Call
Runner.run(agent, prompt)to execute. The agent invokes the appropriate UKG Pro Learning tools and returns structured results. Copy the full example on the right to get started.
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="UKG Pro Learning Agent",
instructions="You have access to UKG Pro Learning tools.",
mcp_servers=[server],
)
result = await Runner.run(agent, "List recent transactions")
print(result.final_output)
asyncio.run(main())Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by UKG Pro Learning. 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.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about UKG Pro Learning MCP in OpenAI Agents SDK
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