Integrating LlamaIndex with SAP Ariba Procurement 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 SAP Ariba Procurement data as native tools without writing custom connectors.

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

Before LlamaIndex can access SAP Ariba Procurement, a SAP Ariba Procurement 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 SAP Ariba Procurement
3. Enter the necessary authentication properties to connect to SAP Ariba Procurement

   In order to connect with SAP Ariba Procurement, set the following:

   • ANID: Your Ariba Network ID.
   • ANID: Specify which API you would like the provider to retrieve SAP Ariba data from. Select the Buyer or Supplier API based on your business role (possible values are PurchaseOrdersBuyerAPIV1 or PurchaseOrdersSupplierAPIV1).
   • Environment: Indicate whether you are connecting to a test or production environment (possible values are TEST or PRODUCTION).

   Authenticating with OAuth

   After setting connection properties, you need to configure OAuth connectivity to authenticate.

   • Set AuthScheme to OAuthClient.
   • Register an application with the service to obtain the APIKey, OAuthClientId and OAuthClientSecret.

     For more information on creating an OAuth application, refer to the Help documentation.

   Automatic OAuth

   After setting the following, you are ready to connect:

   APIKey: The Application key in your app settings. OAuthClientId: The OAuth Client Id in your app settings. OAuthClientSecret: The OAuth Secret in your app settings.

   When you connect, the provider automatically completes the OAuth process:

   1. The provider obtains an access token from SAP Ariba and uses it to request data.
   2. The provider refreshes the access token automatically when it expires.
   3. The OAuth values are saved in memory relative to the location specified in OAuthSettingsLocation.
   
4. Click Save & Test
5. Once authenticated, open the Permissions tab in the SAP Ariba Procurement 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 SAP Ariba Procurement connection configured and a PAT generated, LlamaIndex is prepared to connect to SAP Ariba Procurement 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 SAPAribaProcurement1?"  # 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 SAP Ariba Procurement 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 SAP Ariba Procurement?")
4. The agent reasons over the available tools, calls

   queryData

   against SAP Ariba Procurement, and responds with the result

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