Integrate OpenCode with Live Azure Data Lake Storage Data via CData Connect AI

OpenCode is an open source AI coding agent that connects to a wide range of LLM providers. It supports the model context protocol (MCP), allowing you to configure local or remote MCP servers in its configuration files to add external tools and data sources and give the agent access to live data.

By integrating OpenCode with CData Connect AI through the built-in MCP Server, OpenCode 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, generate the required personal access token, install OpenCode, add the Connect AI MCP Server in the project configuration file, configure an LLM provider, and verify the integration by querying live Azure Data Lake Storage data from OpenCode.

Step 1: Configure Azure Data Lake Storage connectivity for OpenCode

Connectivity to Azure Data Lake Storage from OpenCode is made possible through Connect AI's Remote MCP Server. To interact with Azure Data Lake Storage data from OpenCode, 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

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from OpenCode. It is best practice to create a separate PAT for each integration to maintain granular access control.

1. Click the gear icon () at the top right of the Connect AI app to open Settings
2. On the Settings page, go to the Access Tokens section and click Create PAT
3. Give the PAT a descriptive name and click Create

4. Copy the token when displayed and store it securely. It will not be shown again

With the Azure Data Lake Storage connection configured and a PAT generated, OpenCode can now connect to Azure Data Lake Storage data through Connect AI.

Step 2: Install OpenCode and configure the Connect AI MCP Server

Next, install OpenCode, add the Connect AI Remote MCP Server in your project configuration file, and configure an LLM provider so the agent can discover and call live data tools through Connect AI.

1. Download and install OpenCode by following the official installation guide
2. Create a new folder for your project, or open an existing project directory
3. In the root of that directory, create a file named opencode.json and paste the following configuration:

   {
     "$schema": "https://opencode.ai/config.json",
     "mcp": {
       "cdata-connect-ai": {
         "type": "remote",
         "url": "https://mcp.cloud.cdata.com/mcp",
         "enabled": true,
         "oauth": false,
         "headers": {
           "Authorization": "Basic your_base64_encoded_email_PAT"
         },
         "timeout": 30000
       }
     }
   }
   		

   Note: OpenCode will use Basic authentication with Connect AI. Combine your Connect AI user email and the PAT you created earlier in the format email:PAT, base64 encode the combined string, and prefix it with Basic. For example, given [email protected]:ABC123...XYZ789, the Authorization header value becomes something like: Basic dXNlckBkb21haW4uY29tOkFCQzEyMy4uLlhZWjc4OQ==

4. Open the project directory in OpenCode. Select the project from the project dropdown, or click Add project to point OpenCode at the folder that contains opencode.json
5. Configure an LLM provider so the agent can interpret prompts and call MCP tools. Open the model selector (Ctrl + '), choose a provider such as OpenAI, Anthropic, or Google, and enter your API key

With the MCP server added in opencode.json and an LLM provider configured, OpenCode is ready to query live Azure Data Lake Storage data through Connect AI.

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

With the integration complete, use OpenCode to interact with live Azure Data Lake Storage data through natural language prompts handled by the configured LLM.

1. In a new session, type a prompt in the chat input, for example:
   • Use the cdata-connect-ai tools to list all available catalogs
   • Show the available schemas and tables for Azure Data Lake Storage
   • Query the top 5 records from a table in Azure Data Lake Storage
2. OpenCode calls the Connect AI MCP Server and returns live results from Azure Data Lake Storage data

At this point, OpenCode communicates with the Connect AI MCP Server and retrieves live Azure Data Lake Storage data through remote MCP tools directly from your AI coding agent.

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