Integrating LlamaIndex with ADP Data via CData Connect AI

Leverage the CData Connect AI Remote MCP Server to enable LlamaIndex ReAct agents to securely access and act on ADP data in real time.

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 ADP data as native tools without writing custom connectors.

CData Connect AI offers a secure, low-code environment to connect ADP 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 ADP connectivity in CData Connect AI, register the MCP server with LlamaIndex, and build a ReAct agent that queries ADP data in real time.

Prerequisites

Step 1: Configure ADP Connectivity for LlamaIndex

Before LlamaIndex can access ADP, a ADP 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
  2. From the available data sources, choose ADP
  3. Enter the necessary authentication properties to connect to ADP

    Connect to ADP by specifying the following properties:

    • OAuthClientId: The client Id of the custom OAuth application you obtained from ADP.
    • OAuthClientSecret: The custom OAuth application's client secret.
    • SSLClientCert: Set this to the certificate provided during registration.
    • SSLClientCertPassword: Set this to the password of the certificate.
    • UseUAT: The connector makes requests to the production environment by default. If using a developer account, set UseUAT = true.
    • RowScanDepth: The maximum number of rows to scan for the custom fields columns available in the table. The default value will be set to 100. Setting a high value may decrease performance.

    The connector uses OAuth to authenticate with ADP. OAuth requires the authenticating user to interact with ADP using the browser. OAuth access can be configured in ADP through ADP API Central. For more information, refer ADP's API Central Quick Start Guide and the OAuth section in CData's Help documentation.

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

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
  4. Copy the token and store it securely. The PAT will only be visible during creation

With the ADP connection configured and a PAT generated, LlamaIndex is prepared to connect to ADP 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 ADP1?"  # 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 ADP 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 ADP?")
  4. The agent reasons over the available tools, calls 
    queryData
    against ADP, and responds with the result

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