Integrating LlamaIndex with AlloyDB Data via CData Connect AI

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

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

Prerequisites

Step 1: Configure AlloyDB Connectivity for LlamaIndex

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

    The following connection properties are usually required in order to connect to AlloyDB.

    • Server: The host name or IP of the server hosting the AlloyDB database.
    • User: The user which will be used to authenticate with the AlloyDB server.
    • Password: The password which will be used to authenticate with the AlloyDB server.

    You can also optionally set the following:

    • Database: The database to connect to when connecting to the AlloyDB Server. If this is not set, the user's default database will be used.
    • Port: The port of the server hosting the AlloyDB database. This property is set to 5432 by default.

    Authenticating with Standard Authentication

    Standard authentication (using the user/password combination supplied earlier) is the default form of authentication.

    No further action is required to leverage Standard Authentication to connect.

    Authenticating with pg_hba.conf Auth Schemes

    There are additional methods of authentication available which must be enabled in the pg_hba.conf file on the AlloyDB server.

    Find instructions about authentication setup on the AlloyDB Server here.

    Authenticating with MD5 Authentication

    This authentication method must be enabled by setting the auth-method in the pg_hba.conf file to md5.

    Authenticating with SASL Authentication

    This authentication method must be enabled by setting the auth-method in the pg_hba.conf file to scram-sha-256.

    Authenticating with Kerberos

    The authentication with Kerberos is initiated by AlloyDB Server when the ∏ is trying to connect to it. You should set up Kerberos on the AlloyDB Server to activate this authentication method. Once you have Kerberos authentication set up on the AlloyDB Server, see the Kerberos section of the help documentation for details on how to authenticate with Kerberos. Configuring a connection (Salesforce is shown)

  4. Click Save & Test
  5. Once authenticated, open the Permissions tab in the AlloyDB 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 AlloyDB connection configured and a PAT generated, LlamaIndex is prepared to connect to AlloyDB 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 AlloyDB1?"  # 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 AlloyDB 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 AlloyDB?")
  4. The agent reasons over the available tools, calls 
    queryData
    against AlloyDB, and responds with the result

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