Interact with Live Azure Data Lake Storage Data in Meta Muse via CData Connect AI

Muse is Meta's personal AI agent for iOS, Android, and the web, built on the Muse Spark models. It connects to remote model context protocol (MCP) servers, so you can point Muse at a governed data source and let it discover and call live data tools.

Connect Muse to CData Connect AI through its Remote MCP Server, and Muse gains governed, real-time access to live Azure Data Lake Storage data. You can explore data, fetch records, and interact with objects using natural language prompts, with every request running against authorized sources.

This article shows you how to configure Azure Data Lake Storage connectivity in Connect AI, generate a personal access token, create a custom connector to the Connect AI Remote MCP Server in Muse, and interact with live Azure Data Lake Storage data from Muse.

Step 1: Configure Azure Data Lake Storage connectivity for Meta Muse

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

Step 2: Create a custom connector to the Connect AI MCP Server in Meta Muse

Muse has no manual "add MCP server" form. Instead, you ask Muse in plain language to build a custom connector that points at the Connect AI Remote MCP Server, a hosted, public HTTPS endpoint that speaks streamable HTTP. Muse builds the connector on its secure VM using the official MCP SDK, then discovers and calls the available Connect AI data tools.

1. Open Muse on iOS, Android, or the web and start a new chat
2. Ask Muse to build a custom connector to the Connect AI Remote MCP Server, giving it the server URL and noting that the endpoint is a hosted MCP server over streamable HTTP, for example:
   Create a custom connector to CData Connect AI. It is a hosted MCP server over streamable HTTP at https://mcp.cloud.cdata.com/mcp and it requires an Authorization header.
3. When Muse prompts for credentials, it opens a secure entry flow, separate from the chat, that saves the value in its Secure Credentials Store. Enter the Authorization header value in the format Basic your_base64_encoded_email_PAT

   Note: For the Authorization value, base64 encode email:PAT and prefix it with Basic. Enter it only in Muse's secure credential prompt, never in the chat.

4. Muse connects to the Connect AI MCP Server and lists the available data tools. Review the custom connector confirmation, then click Continue. When Muse offers to save the integration as a skill, choose yes so the connector is available in every future chat

With the custom connector saved, the Connect AI tools are available to Muse in any chat.

Step 3: Interact with live Azure Data Lake Storage data from Meta Muse

With the integration complete, interact with live Azure Data Lake Storage data directly from Muse through natural language prompts.

1. In a Muse chat, type a prompt that uses the Connect AI connector, for example:
   • Use the CData Connect AI connector to list all available connections
   • Show the available objects and fields for Azure Data Lake Storage
   • Fetch the top 5 records from Azure Data Lake Storage
2. Approve the tool call in the Sentinel dialog when prompted
3. Muse calls the Connect AI MCP Server and returns live results from Azure Data Lake Storage data

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

Get started with CData Connect AI

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