Integrating LlamaIndex with Adobe Commerce 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 Adobe Commerce data as native tools without writing custom connectors.

CData Connect AI offers a secure, low-code environment to connect Adobe Commerce 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 Adobe Commerce connectivity in CData Connect AI, register the MCP server with LlamaIndex, and build a ReAct agent that queries Adobe Commerce 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 Adobe Commerce Connectivity for LlamaIndex

Before LlamaIndex can access Adobe Commerce, a Adobe Commerce 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 Adobe Commerce 👁 Selecting a data source
   
3. Enter the necessary authentication properties to connect to Adobe Commerce

   Adobe Commerce uses the OAuth 1 authentication standard. To connect to the Adobe Commerce REST API, obtain values for the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties by registering an app with your Adobe Commerce system. See the "Getting Started" section in the help documentation for a guide to obtaining the OAuth values and connecting.

   You will also need to provide the URL to your Adobe Commerce system. The URL depends on whether you are using the Adobe Commerce REST API as a customer or administrator.

   • Customer: To use Adobe Commerce as a customer, make sure you have created a customer account in the Adobe Commerce homepage. To do so, click Account -> Register. You can then set the URL connection property to the endpoint of your Adobe Commerce system.

   • Administrator: To access Adobe Commerce as an administrator, set CustomAdminPath instead. This value can be obtained in the Advanced settings in the Admin menu, which can be accessed by selecting System -> Configuration -> Advanced -> Admin -> Admin Base URL.

     If the Use Custom Admin Path setting on this page is set to YES, the value is inside the Custom Admin Path text box; otherwise, set the CustomAdminPath connection property to the default value, which is "admin".

   👁 Configuring a connection (Salesforce is shown)
   
4. Click Save & Test
5. Once authenticated, open the Permissions tab in the Adobe Commerce 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 Adobe Commerce connection configured and a PAT generated, LlamaIndex is prepared to connect to Adobe Commerce 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 Adobe Commerce1?" # 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 Adobe Commerce 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 Adobe Commerce?")
4. The agent reasons over the available tools, calls

   queryData

   against Adobe Commerce, and responds with the result

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