Integrate Devin with Live Azure Data Lake Storage Data Using CData Connect AI

Devin is an AI software engineer from Cognition that plans, writes, and tests code in autonomous sessions. It supports the model context protocol (MCP) through its built-in MCP marketplace, allowing you to install remote MCP servers and give Devin access to external tools and live data sources.

By integrating Devin with CData Connect AI through the built-in MCP Server, Devin gains governed, real-time access to live Azure Data Lake Storage data. You can list catalogs, explore schemas, and query records from Azure Data Lake Storage data using natural language prompts, with all data access running securely against authorized sources.

This article explains how to configure Azure Data Lake Storage connectivity in Connect AI, install the CData Connect AI MCP Server from the Devin MCP marketplace, and verify the integration by querying live Azure Data Lake Storage data from a Devin session.

Step 1: Configure Azure Data Lake Storage connectivity for Devin

Connectivity to Azure Data Lake Storage from Devin is made possible through Connect AI's Remote MCP Server. To interact with Azure Data Lake Storage data from Devin, start by creating and configuring a Azure Data Lake Storage connection in Connect AI.

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

2. Select Azure Data Lake Storage from the Add Connection panel

3. Enter the necessary authentication properties to connect to Azure Data Lake Storage.

   Authenticating to a Gen 1 DataLakeStore Account

   Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

   For this, an Active Directory web application is required. You can create one as follows:

   1. Sign in to your Azure Account through the .
   2. Select "Entra ID" (formerly Azure AD).
   3. Select "App registrations".
   4. Select "New application registration".
   5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
   6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
   7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

   To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen1.
   • Account: Set this to the name of the account.
   • OAuthClientId: Set this to the application Id of the app you created.
   • OAuthClientSecret: Set this to the key generated for the app you created.
   • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

   Authenticating to a Gen 2 DataLakeStore Account

   To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen2.
   • Account: Set this to the name of the account.
   • FileSystem: Set this to the file system which will be used for this account.
   • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
   
4. Click Save & Test
5. Navigate to the Permissions tab and update user-based permissions

With the Azure Data Lake Storage connection configured, Devin can now connect to Azure Data Lake Storage data through Connect AI.

Step 2: Install the Connect AI MCP Server in Devin

Next, install the CData Connect AI MCP Server from the Devin MCP servers list so your sessions can discover and call live data tools through Connect AI.

Note: Installing MCP servers in Devin requires the Manage MCP Servers permission. If you don't have this permission, use the Suggest option or contact your organization admin.

1. Log into Devin and navigate to Settings and Connections, then select the MCP servers tab
2. Search for CData Connect AI in the marketplace and select the verified listing

3. Click Install and enable. When prompted, complete the OAuth flow to authenticate with your Connect AI account
4. Once installed, confirm the Enable in sessions toggle is on and click Test tools. Devin validates the connection and lists the available Connect AI tools, such as getCatalogs, getSchemas, getTables, and queryData

With the MCP server installed and enabled, the Connect AI tools are available to every Devin session in your workspace.

Step 3: Query live Azure Data Lake Storage data from Devin

With the integration complete, start a Devin session and interact with live Azure Data Lake Storage data through natural language prompts.

1. From the Devin home page, start a new session
2. In the session prompt, ask Devin to work with your Connect AI data, for example:
   • Can you list the connections in CData Connect AI
   • Show the available schemas and tables for Azure Data Lake Storage
   • Query the top 5 records from a table in Azure Data Lake Storage
3. Devin calls the Connect AI MCP Server and returns live results from Azure Data Lake Storage data directly in the session. You can follow the tool calls, inputs, and outputs in the Progress panel

At this point, Devin communicates with the Connect AI MCP Server and retrieves live Azure Data Lake Storage data through remote MCP tools directly from your sessions, whether you're exploring data, building features against it, or automating engineering tasks.

Get started with CData Connect AI

To access hundreds of SaaS, big data, and NoSQL sources directly from your cloud applications, try CData Connect AI today. Download a free 14-day trial of CData Connect AI, and our Support Team is available to help with any questions you have.