Integrating LlamaIndex with HubDB Data via CData Connect AI

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

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

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

Log in to Connect AI, click Sources, and then click + Add Connection
From the available data sources, choose HubDB
Enter the necessary authentication properties to connect to HubDB
There are two authentication methods available for connecting to HubDB data source: OAuth Authentication with a public HubSpot application and authentication with a Private application token.

Using a Custom OAuth App

AuthScheme must be set to "OAuth" in all OAuth flows. Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).

Follow the steps below to register an application and obtain the OAuth client credentials:
1. Log into your HubSpot app developer account.
  - Note that it must be an app developer account. Standard HubSpot accounts cannot create public apps.
2. On the developer account home page, click the Apps tab.
3. Click Create app.
4. On the App info tab, enter and optionally modify values that are displayed to users when they connect. These values include the public application name, application logo, and a description of the application.
5. On the Auth tab, supply a callback URL in the "Redirect URLs" box.
  - If you're creating a desktop application, set this to a locally accessible URL like http://localhost:33333.
  - If you are creating a Web application, set this to a trusted URL where you want users to be redirected to when they authorize your application.
6. Click Create App. HubSpot then generates the application, along with its associated credentials.
7. On the Auth tab, note the Client ID and Client secret. You will use these later to configure the driver.
8. Under Scopes, select any scopes you need for your application's intended functionality.
  
  A minimum of the following scopes is required to access tables:
  - hubdb
  - oauth
  - crm.objects.owners.read
9. Click Save changes.
10. Install the application into a production portal with access to the features that are required by the integration.
  - Under "Install URL (OAuth)", click Copy full URL to copy the installation URL for your application.
  - Navigate to the copied link in your browser. Select a standard account in which to install the application.
  - Click Connect app. You can close the resulting tab.
Using a Private App

To connect using a HubSpot private application token, set the AuthScheme property to "PrivateApp."

You can generate a private application token by following the steps below:
1. In your HubDB account, click the settings icon (the gear) in the main navigation bar.
2. In the left sidebar menu, navigate to Integrations > Private Apps.
3. Click Create private app.
4. On the Basic Info tab, configure the details of your application (name, logo, and description).
5. On the Scopes tab, select Read or Write for each scope you want your private application to be able to access.
6. A minimum of hubdb and crm.objects.owners.read is required to access tables.
7. After you are done configuring your application, click Create app in the top right.
8. Review the info about your application's access token, click Continue creating, and then Show token.
9. Click Copy to copy the private application token.
To connect, set PrivateAppToken to the private application token you retrieved.
Click Save & Test
Once authenticated, open the Permissions tab in the HubDB 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.

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

With the HubDB connection configured and a PAT generated, LlamaIndex is prepared to connect to HubDB 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.

Create a folder for the LlamaIndex MCP project
Create two Python files within the folder:
```
config.py
```
and
```
llamaindex_agent.py
```
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.

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 HubDB1?"  # 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 HubDB using LlamaIndex (via the MCP server)

When the installation finishes, run
```
python llamaindex_agent.py
```
to execute the script
The script connects to the MCP server and discovers the CData Connect AI MCP tools available for querying your connected data
Supply a prompt (e.g., "How many tables are available in HubDB?")
The agent reasons over the available tools, calls
```
queryData
```
against HubDB, and responds with the result

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Integrating LlamaIndex with HubDB Data via CData Connect AI

Prerequisites

Step 1: Configure HubDB Connectivity for LlamaIndex

Using a Custom OAuth App

Using a Private App