How to Use the UKG Pro Learning MCP in LangChain

Q: How do I check an employee's status using UKG Pro Learning and LangChain?

Your chain should first call the users tool to confirm identity. Next, run the user ID through the enrollments tool. This gives you a definitive list of their current learning records.

Q: Can I use UKG Pro Learning and LangChain to find required courses?

Yes. You can call curricula first to see the high-level paths. Then, extract relevant IDs from that output and pass them one by one into the courses tool for detailed information.

Build complex reasoning chains for UKG Pro Learning using LangChain.

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Connect UKG Pro Learning MCP to LangChain

Create your Vinkius account to connect UKG Pro Learning to LangChain 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 UKG Pro Learning with LangChain

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Claude

Perplexity

Chaining Logic Across MCP Server Tools

The `users` tool gets specific employee details. You can then pass that user ID to the `enrollments` tool, checking their current status against the system. This sequence lets your agent build a full picture: it knows who the person is and what training they've already logged.

Automating Learning Path Discovery

Need to map out available content? Start by listing all tracking curricula with `curricula`. Then, use a curriculum ID to check for related required courses via the `courses` tool. This flow lets your agent automatically generate suggested learning paths based on organizational requirements.

Orchestrating User Training Audits

To audit compliance, first use `users` to get a roster of employee IDs. Feed those IDs into the `enrollments` tool in a loop to pull all current record statuses. This setup lets your agent rapidly collect and structure data for management reporting.

Setup guide

Set up UKG Pro Learning MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes UKG Pro Learning tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "ukg-pro-learning-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent UKG Pro Learning transactions"
    })
    print(result["messages"][-1].content)

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Common questions about UKG Pro Learning MCP in LangChain

Your chain should first call the `users` tool to confirm identity. Next, run the user ID through the `enrollments` tool. This gives you a definitive list of their current learning records.

Yes. You can call `curricula` first to see the high-level paths. Then, extract relevant IDs from that output and pass them one by one into the `courses` tool for detailed information.

This server primarily handles user identity, course definitions, curriculum structures, and enrollment records. It's all about employee training status within UKG Pro Learning.

Absolutely. Because you build the logic chain yourself, your agent can jump between `users`, `enrollments`, and `curricula` in any order necessary to answer complex questions.

You get full control over the execution path. You aren't limited by a single API call; you build multi-step reasoning pipelines where the agent decides WHICH tool to call and in WHAT ORDER.

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