Interact with Live Google Cloud 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 Google Cloud 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 Google Cloud 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 Google Cloud Storage data from Muse.
Step 1: Configure Google Cloud Storage connectivity for Meta Muse
Connectivity to Google Cloud Storage from Muse is made possible through Connect AI's Remote MCP Server. To interact with Google Cloud Storage data from Muse, start by creating and configuring a Google Cloud Storage connection in Connect AI.
- Log into Connect AI, click Sources, and then click Add Connection
- Select Google Cloud Storage from the Add Connection panel
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Enter the necessary authentication properties to connect to Google Cloud Storage.
Authenticate with a User Account
You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.
When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes
Authenticate with a Service Account
Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.
You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:
- InitiateOAuth: Set this to GETANDREFRESH.
- OAuthJWTCertType: Set this to "PFXFILE".
- OAuthJWTCert: Set this to the path to the .p12 file you generated.
- OAuthJWTCertPassword: Set this to the password of the .p12 file.
- OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
- OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
- OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
- ProjectId: Set this to the Id of the project you want to connect to.
The OAuth flow for a service account then completes.
- Click Save & Test
- 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.
- Click the gear icon () at the top right of the Connect AI app to open Settings
- On the Settings page, go to the Access Tokens section and click Create PAT
- Give the PAT a descriptive name and click Create
- Copy the token when displayed and store it securely. It will not be shown again
With the Google Cloud Storage connection configured and a PAT generated, Muse can now connect to Google Cloud 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.
- Open Muse on iOS, Android, or the web and start a new chat
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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.
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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.
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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 Google Cloud Storage data from Meta Muse
With the integration complete, interact with live Google Cloud Storage data directly from Muse through natural language prompts.
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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 Google Cloud Storage
- Fetch the top 5 records from Google Cloud Storage
- Approve the tool call in the Sentinel dialog when prompted
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Muse calls the Connect AI MCP Server and returns live results from Google Cloud Storage data
At this point, Muse communicates with the Connect AI MCP Server and retrieves live Google Cloud Storage data through remote MCP tools directly from your chats.
Get started with CData Connect AI
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