Integrating LlamaIndex with Paylocity Data via CData Connect AI

LlamaIndex is a data framework for building LLM applications — agents, RAG pipelines, and structured workflows that reason over external data. By integrating LlamaIndex with CData Connect AI through the built-in MCP Server, your agents can discover and query live Paylocity data as native tools without writing custom connectors.

CData Connect AI offers a secure, low-code environment to connect Paylocity and other data sources, removing the need for complex ETL and enabling seamless automation across business applications with live data.

This article outlines how to configure Paylocity connectivity in CData Connect AI, register the MCP server with LlamaIndex, and build a ReAct agent that queries Paylocity data in real time.

Prerequisites

• An account in CData Connect AI
• Python version 3.10 or higher, to install the LlamaIndex packages
• Generate and save an OpenAI API key
• Install Visual Studio Code in your system

Step 1: Configure Paylocity Connectivity for LlamaIndex

Before LlamaIndex can access Paylocity, a Paylocity connection must be created in CData Connect AI. This connection is then exposed to LlamaIndex through the remote MCP server.

1. Log in to Connect AI, click Sources, and then click + Add Connection 👁 Adding a Connection
   
2. From the available data sources, choose Paylocity 👁 Selecting a data source
   
3. Enter the necessary authentication properties to connect to Paylocity

   Set the following to establish a connection to Paylocity:

   • RSAPublicKey: Set this to the RSA Key associated with your Paylocity, if the RSA Encryption is enabled in the Paylocity account.

     This property is required for executing Insert and Update statements, and it is not required if the feature is disabled.

   • UseSandbox: Set to true if you are using sandbox account.
   • CustomFieldsCategory: Set this to the Customfields category. This is required when IncludeCustomFields is set to true. The default value for this property is PayrollAndHR.
   • Key: The AES symmetric key(base 64 encoded) encrypted with the Paylocity Public Key. It is the key used to encrypt the content.

     Paylocity will decrypt the AES key using RSA decryption.
     It is an optional property if the IV value not provided, The driver will generate a key internally.

   • IV: The AES IV (base 64 encoded) used when encrypting the content. It is an optional property if the Key value not provided, The driver will generate an IV internally.

   Connect Using OAuth Authentication

   You must use OAuth to authenticate with Paylocity. OAuth requires the authenticating user to interact with Paylocity using the browser. For more information, refer to the OAuth section in the Help documentation.

   The Pay Entry API

   The Pay Entry API is completely separate from the rest of the Paylocity API. It uses a separate Client ID and Secret, and must be explicitly requested from Paylocity for access to be granted for an account. The Pay Entry API allows you to automatically submit payroll information for individual employees, and little else. Due to the extremely limited nature of what is offered by the Pay Entry API, we have elected not to give it a separate schema, but it may be enabled via the UsePayEntryAPI connection property.

   Please be aware that when setting UsePayEntryAPI to true, you may only use the CreatePayEntryImportBatch & MergePayEntryImportBatchgtable stored procedures, the InputTimeEntry table, and the OAuth stored procedures. Attempts to use other features of the product will result in an error. You must also store your OAuthAccessToken separately, which often means setting a different OAuthSettingsLocation when using this connection property. 👁 Configuring a connection (Salesforce is shown)
   

4. Click Save & Test
5. Once authenticated, open the Permissions tab in the Paylocity connection and configure user-based permissions as required 👁 Updating permissions
   

Generate a Personal Access Token (PAT)

LlamaIndex authenticates to Connect AI using an account email and a Personal Access Token (PAT). Creating separate PATs for each integration is recommended to maintain access control granularity.

1. In Connect AI, select the Gear icon in the top-right to open Settings
2. Under Access Tokens, select Create PAT
3. Provide a descriptive name for the token and select Create 👁 Creating a new PAT
   
4. Copy the token and store it securely. The PAT will only be visible during creation

With the Paylocity connection configured and a PAT generated, LlamaIndex is prepared to connect to Paylocity data through the CData MCP server.

Step 2: Connect to the MCP server in LlamaIndex

To connect LlamaIndex with CData Connect AI Remote MCP Server and use OpenAI for reasoning, configure your MCP server endpoint and authentication in a

config.py

file. These values let LlamaIndex’s MCP tool spec call the MCP server tools, while OpenAI handles the natural language reasoning.

1. Create a folder for the LlamaIndex MCP project
2. Create two Python files within the folder:

   config.py

   and

   llamaindex_agent.py

3. In

   config.py

   , define your MCP server URL and your Base64-encoded CData Connect AI email and PAT (obtained in the prerequisites):

   class Config:
   
    MCP_BASE_URL = "https://mcp.cloud.cdata.com/mcp" # MCP Server URL
    MCP_AUTH = "base64encoded(EMAIL:PAT)" # Base64 encoded Connect AI Email:PAT
   

   Note: You can create the base64 encoded version of MCP_AUTH using any Base64 encoding tool.

4. In

   llamaindex_agent.py

   , wire up the MCP tool spec and a ReAct agent:

   """
   Integrates a LlamaIndex ReAct agent with the CData Connect AI MCP server.
   The script discovers MCP tools, wraps them as LlamaIndex tools, and runs an
   agent loop driven by OpenAI for reasoning.
   """
   
   import asyncio
   from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
   from llama_index.core.agent.workflow import ReActAgent
   from llama_index.llms.openai import OpenAI
   from config import Config
   
   async def main():
   
    # Initialize the MCP client pointed at Connect AI
    mcp_client = BasicMCPClient(
    Config.MCP_BASE_URL,
    headers={"Authorization": f"Basic {Config.MCP_AUTH}"},
    )
   
    # Discover tools the MCP server exposes (getCatalogs, queryData, etc.)
    tool_spec = McpToolSpec(client=mcp_client)
    tools = await tool_spec.to_tool_list_async()
    print("Discovered MCP tools:", [t.metadata.name for t in tools])
   
    # Configure the LLM that drives the ReAct loop
    llm = OpenAI(
    model="gpt-4o",
    temperature=0.2,
    api_key="YOUR_OPENAI_API_KEY", # https://platform.openai.com/
    )
   
    # Build the agent with the MCP-backed tools
    agent = ReActAgent(tools=tools, llm=llm)
   
    user_prompt = "How many tables are available in Paylocity1?" # Change as needed
    print(f"
   User prompt: {user_prompt}")
   
    response = await agent.run(user_prompt)
   
    print("Agent final response:", response)
   
   if __name__ == "__main__":
    asyncio.run(main())
   

Step 3: Install the LlamaIndex packages

Since this workflow uses LlamaIndex together with the CData Connect AI MCP server and OpenAI for reasoning, install the required Python packages.

Run the following command in your project terminal:

pip install llama-index llama-index-tools-mcp llama-index-llms-openai

Step 4: Prompt Paylocity using LlamaIndex (via the MCP server)

1. When the installation finishes, run

   python llamaindex_agent.py

   to execute the script
2. The script connects to the MCP server and discovers the CData Connect AI MCP tools available for querying your connected data
3. Supply a prompt (e.g., "How many tables are available in Paylocity?")
4. The agent reasons over the available tools, calls

   queryData

   against Paylocity, and responds with the result

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