How to Use the Workday MCP in LlamaIndex

Q: How does LlamaIndex handle complex reporting in Workday?

You run executeraasreport and then index the resulting JSON data. This makes the report's contents part of your searchable knowledge base, allowing you to ask follow-up questions against that specific dataset.

Q: If I want to search job requisitions in Workday using LlamaIndex, how do I proceed?

First, call listjobrequisitions. Then, you pass that output—the list of open roles—into your index. This lets you query the system about specific jobs later, grounding the answer in live API data.

Q: Does LlamaIndex support multi-step queries with Workday?

Yes. You can combine results from multiple tools (like getworker and listworkers) into a single index, enabling deep context retrieval that goes beyond simple API calls.

Ground your Workday knowledge base with LlamaIndex using MCP Server for accurate data retrieval.

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Connect Workday MCP to LlamaIndex

Create your Vinkius account to connect Workday 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 Workday with LlamaIndex

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Indexing Payroll and Time Off Data

Don't just execute; index the results. Running `get_payroll_results` converts raw payroll numbers into searchable knowledge chunks. This means you can query past sessions to answer questions about historical compensation data.

Building Knowledge from Organizational Data

Need context on company structure? Use the server's ability to list supervisory organizations via `list_supervisory_orgs`. Indexing this allows you to ask, 'Which managers oversee CostCenter X?' and get an answer grounded in Workday data.

Searching Worker Records Across Time

The `get_worker` tool pulls a specific profile. By indexing these profiles, your RAG application can build a searchable knowledge base of employee details. You can then query the system later to retrieve facts about that worker's history.

Setup guide

Set up Workday 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 Workday 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 Workday tools.",
)
response = await agent.run("List recent Workday data")

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Common questions about Workday MCP in LlamaIndex

You run `execute_raas_report` and then index the resulting JSON data. This makes the report's contents part of your searchable knowledge base, allowing you to ask follow-up questions against that specific dataset.

The server allows indexing payroll data, time off balances (via `get_time_off_balances`), and organizational structures. Anything returned by the tools becomes a verifiable source for your index.

First, call `list_job_requisitions`. Then, you pass that output—the list of open roles—into your index. This lets you query the system about specific jobs later, grounding the answer in live API data.

Yes. You can combine results from multiple tools (like `get_worker` and `list_workers`) into a single index, enabling deep context retrieval that goes beyond simple API calls.

The server exposes detailed worker records, including the employee ID, name, and full profile details retrieved through `get_worker`.

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