How to Use the UKG Pro Learning MCP in LlamaIndex

Q: Can I use LlamaIndex to analyze UKG Pro Learning enrollment records?

Yes, you can run the enrollments tool and then index that output. Your agent can later query this indexed historical data, allowing for deeper pattern recognition than a simple API call.

Q: Does the MCP Server need multiple calls to populate my index with UKG Pro Learning data?

Yes. You run a tool like users or curricula multiple times, and LlamaIndex collects those distinct outputs into one unified, queryable index.

Build knowledge bases from UKG Pro Learning data using LlamaIndex.

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Connect UKG Pro Learning MCP to LlamaIndex

Create your Vinkius account to connect UKG Pro Learning to LlamaIndex 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 LlamaIndex

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Indexing User and Course Data

The `users` tool gets employee details, but you don't want to query it every time. Indexing the output of `users` means that information becomes part of your searchable knowledge base. You can then ask complex questions about staff profiles without needing a live API call—the answer is grounded in the data index.

Creating Semantic Search for Curricula

List all learning paths using `curricula`. By indexing this output, you build a knowledge graph of your company's training structure. You can query past curricula and get answers grounded in actual API data, not guesses. This is perfect for helping new hires find the right path.

Querying Enrollment History via MCP Server

Instead of just getting live enrollment lists from `enrollments`, you index those results. This lets your agent answer questions like, 'What were John Doe's required courses last quarter?' The resulting data is available for semantic search across sessions.

Setup guide

Set up UKG Pro Learning MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all UKG Pro Learning MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to UKG Pro Learning tools.",
)
response = await agent.run("List recent UKG Pro Learning data")

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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 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.

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Common questions about UKG Pro Learning MCP in LlamaIndex

It takes the live API results—like specific course details from `courses`—and turns them into a searchable knowledge index. You query past sessions or configurations and get answers grounded in actual data.

Yes, you can run the `enrollments` tool and then index that output. Your agent can later query this indexed historical data, allowing for deeper pattern recognition than a simple API call.

The server handles user details, courses, curriculum structures, and enrollment records. LlamaIndex makes these specific types of structured data available for deep semantic retrieval.

Yes. You run a tool like `users` or `curricula` multiple times, and LlamaIndex collects those distinct outputs into one unified, queryable index.

It combines live API data from the MCP Server with your existing documents. You get answers that are grounded in both what's written and what's currently available via the tools.

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