How to Use the UKG Pro MCP in LlamaIndex

Q: What data does this UKG Pro server touch regarding payroll?

The payroll tool accesses current pay period records containing financial transaction details. Because LlamaIndex indexes these results, the knowledge base holds salary and deduction amounts for historical reference.

Q: Can I search previous employee data using UKG Pro with LlamaIndex?

Yes. You pass the employees tool output into your indexer. The resulting vector store lets you query specific employee profiles or changes that happened in past sessions.

Q: Is this MCP Server good for combining job and organization data?

It's excellent. By indexing the outputs of jobs and organizations, your RAG application can unify these two separate data streams into one comprehensive, queryable index.

Build searchable knowledge bases with UKG Pro data using LlamaIndex.

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Connect UKG Pro MCP to LlamaIndex

Create your Vinkius account to connect UKG Pro 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 with LlamaIndex

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Indexing MCP Server Outputs for Search

LlamaIndex lets you index the results of API calls directly into a vector store. When your agent runs `employees` or `organizations`, the structured data output becomes part of your searchable knowledge base, not just a one-off answer. You can query past session data and get answers grounded in actual UKG Pro records. This is key for building RAG applications that need to reference specific company configurations or employee details from months ago.

Querying Payroll History

Use the `payroll` tool output, and then index it. Instead of just getting this period's payroll data, you can build a searchable index over multiple pay cycles. You query for specific payment amounts or dates to answer questions that require historical context. This makes your AI client an archive, giving semantic search capabilities to sensitive financial records.

Cross-Referencing Job Codes and Organizations

The `jobs` tool provides job configuration details, while `organizations` lists the company structure. By indexing both sets of data, you can ask a question like: 'What salary band is associated with a Senior Analyst role within the Marketing department?' The index links these concepts together for retrieval. This combined knowledge base lets your application answer complex business questions without requiring a rigid tool execution flow.

Setup guide

Set up UKG Pro 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 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 tools.",
)
response = await agent.run("List recent UKG Pro 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. 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 MCP in LlamaIndex

It doesn't just execute the API call; it takes the structured output (like an employee profile) and converts it into vector embeddings. This allows you to query by concept, not just by ID.

The `payroll` tool accesses current pay period records containing financial transaction details. Because LlamaIndex indexes these results, the knowledge base holds salary and deduction amounts for historical reference.

Yes. You pass the `employees` tool output into your indexer. The resulting vector store lets you query specific employee profiles or changes that happened in past sessions.

It's excellent. By indexing the outputs of `jobs` and `organizations`, your RAG application can unify these two separate data streams into one comprehensive, queryable index.

Since you are indexing API responses, the most sensitive data types are financial records (from `payroll`) and personal identification details (from `employees`). Indexing ensures that these results remain grounded in verified API sources.

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