Integrating LlamaIndex with IBM Cloud Object Storage Data via CData Connect AI

Leverage the CData Connect AI Remote MCP Server to enable LlamaIndex ReAct agents to securely access and act on IBM Cloud Object Storage 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 IBM Cloud Object Storage data as native tools without writing custom connectors.

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

Prerequisites

Step 1: Configure IBM Cloud Object Storage Connectivity for LlamaIndex

Before LlamaIndex can access IBM Cloud Object Storage, a IBM Cloud Object Storage 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 IBM Cloud Object Storage
  3. Enter the necessary authentication properties to connect to IBM Cloud Object Storage

    Register a New Instance of Cloud Object Storage

    If you do not already have Cloud Object Storage in your IBM Cloud account, follow the procedure below to install an instance of SQL Query in your account:

    1. Log in to your IBM Cloud account.
    2. Navigate to the page, choose a name for your instance and click Create. You will be redirected to the instance of Cloud Object Storage you just created.

    Connecting using OAuth Authentication

    There are certain connection properties you need to set before you can connect. You can obtain these as follows:

    API Key

    To connect with IBM Cloud Object Storage, you need an API Key. You can obtain this as follows:

    1. Log in to your IBM Cloud account.
    2. Navigate to the Platform API Keys page.
    3. On the middle-right corner click "Create an IBM Cloud API Key" to create a new API Key.
    4. In the pop-up window, specify the API Key name and click "Create". Note the API Key as you can never access it again from the dashboard.

    Cloud Object Storage CRN

    If you have multiple accounts, specify the CloudObjectStorageCRN explicitly. To find the appropriate value, you can:

    • Query the Services view. This will list your IBM Cloud Object Storage instances along with the CRN for each.
    • Locate the CRN directly in IBM Cloud. To do so, navigate to your IBM Cloud Dashboard. In the Resource List, Under Storage, select your Cloud Object Storage resource to get its CRN.

    Connecting to Data

    You can now set the following to connect to data:

    • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
    • ApiKey: Set this to your API key which was noted during setup.
    • CloudObjectStorageCRN (Optional): Set this to the cloud object storage CRN you want to work with. While the connector attempts to retrieve this automatically, specifying this explicitly is recommended if you have more than Cloud Object Storage account.

    When you connect, the connector completes the OAuth process.

    1. Extracts the access token and authenticates requests.
    2. Saves OAuth values in OAuthSettingsLocation to be persisted across connections.
  4. Click Save & Test
  5. Once authenticated, open the Permissions tab in the IBM Cloud Object Storage 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 IBM Cloud Object Storage connection configured and a PAT generated, LlamaIndex is prepared to connect to IBM Cloud Object Storage 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 IBMCloudObjectStorage1?"  # 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 IBM Cloud Object Storage 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 IBM Cloud Object Storage?")
  4. The agent reasons over the available tools, calls 
    queryData
    against IBM Cloud Object Storage, and responds with the result

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