Connecting Mastra with AlloyDB Data via CData Connect AI MCP Server

Mastra is designed for developers and enterprise teams building intelligent, composable AI agents. Its modular framework and declarative architecture make it simple to orchestrate agents, integrate LLMs, and automate data-driven workflows. But when agents need to work with data beyond their local memory or predefined APIs, many implementations rely on custom middleware or scheduled syncs to copy data from external systems into local stores. This approach adds complexity, increases maintenance overhead, introduces latency, and limits the real-time potential of your agents.

CData Connect AI bridges this gap with live, direct connectivity to more than 300 enterprise applications, databases, ERPs, and analytics platforms. Through CData's remote Model Context Protocol (MCP) Server, Mastra agents can securely query, read, and act on real-time data without replication. The result is grounded responses, faster reasoning, and automated decision-making across systems all with stronger governance and fewer moving parts.

This article outlines the steps required to configure CData Connect AI MCP connectivity, register the MCP server in Mastra Studio, and build an agent that queries live AlloyDB data in real time.

Prerequisites

Before starting, make sure you have:

1. A CData Connect AI account
2. Node.js 18+ and npm installed
3. A working Mastra project (created via npm create mastra@latest)
4. Access to AlloyDB

Credentials checklist

Ensure you have these credentials ready for the connection:

1. USERNAME: Your CData email login
2. PAT: Connect AI, go to Settings and click on Access Tokens (copy once)
3. MCP_BASE_URL: https://mcp.cloud.cdata.com/mcp

Step 1: Configure AlloyDB connectivity for Mastra

Connectivity to AlloyDB from Mastra is made possible through CData Connect AI Remote MCP. To interact with AlloyDB data from Mastra, we start by creating and configuring a AlloyDB connection in CData Connect AI.

1. Log into Connect AI, click Sources, and then click Add Connection
👁 Adding a Connection

2. Select "AlloyDB" from the Add Connection panel
👁 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. Navigate to the Permissions tab in the Add AlloyDB Connection page and update the User-based permissions. 👁 Updating permissions
   

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Mastra. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create. 👁 Creating a new PAT
   
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, we are ready to connect to AlloyDB data from Mastra.

Step 2: Set up the Mastra project

• Open a terminal and navigate to your desired folder
• Create a new project:

  npm create mastra@latest

👁 Creating a New Project

• Open the folder in VS Code
• Install the required Mastra dependencies:

  npm install @mastra/core @mastra/libsql @mastra/memory

• Then install the MCP integration package separately:

  npm install @mastra/mcp

Step 3: Configure environment variables

Create a .env file at the project root with the following keys:

OPENAI_API_KEY=sk-...
[email protected]
CDATA_CONNECT_AI_PASSWORD=your_PAT

Restart your dev server after saving changes:

npm run dev

Step 4: Add the CData Connect AI agent

Create a file src/mastra/agents/connect-ai-agent.ts with the following code:

import { Agent } from "@mastra/core/agent";
import { Memory } from "@mastra/memory";
import { LibSQLStore } from "@mastra/libsql";
import { MCPClient } from "@mastra/mcp";

const mcpClient = new MCPClient({
 servers: {
 cdataConnectAI: {
 url: new URL("https://connect.cdata.com/mcp/"),
 requestInit: {
 headers: {
 Authorization: `Basic ${Buffer.from(
 `${process.env.CDATA_CONNECT_AI_USER}:${process.env.CDATA_CONNECT_AI_PASSWORD}`
 ).toString("base64")}`,
 },
 },
 },
 },
});

export const connectAIAgent = new Agent({
 name: "Connect AI Agent",
 instructions: "You are a data exploration and analysis assistant with access to CData Connect AI.",
 model: "openai/gpt-4o-mini",
 tools: await mcpClient.getTools(),
 memory: new Memory({
 storage: new LibSQLStore({ url: "file:../mastra.db" }),
 }),
});

Step 5: Update index.ts to register the agent

Replace the contents of src/mastra/index.ts with:

import { Mastra } from "@mastra/core/mastra";
import { PinoLogger } from "@mastra/loggers";
import { LibSQLStore } from "@mastra/libsql";
import { connectAIAgent } from "./agents/connect-ai-agent.js";

export const mastra = new Mastra({
 agents: { connectAIAgent },
 storage: new LibSQLStore({ url: "file:../mastra.db" }),
 logger: new PinoLogger({ name: "Mastra", level: "info" }),
 observability: { default: { enabled: true } },
});

Step 6: Run and verify the connection

Start your Mastra server:

npm run dev

👁 Start your Mastra server


Step 7: Run a live query in Mastra Studio

In Mastra Studio, open the chat interface and enter one of the following sample prompts:

List available catalogs from my connected data sources.

👁 Run QUery in Mastra Studio


Build real-time, data-aware agents with Mastra and CData

Mastra and CData Connect AI together enable powerful AI-driven workflows where agents have live access to enterprise data and act intelligently without sync pipelines or manual integration logic.

Start your free trial today to see how CData can empower Mastra with live, secure access to hundreds of external systems.