Integrate Goose with Live Bitbucket Data via CData Connect AI

Goose is an open source AI agent that runs locally on your machine and works with a wide range of LLM providers. It supports the model context protocol (MCP) through its extensions framework, so you can add external tools and data sources and give the agent access to live systems beyond the model's training data.

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

This article explains how to configure Bitbucket connectivity in Connect AI, generate the required personal access token, install and set up Goose, add the Connect AI MCP Server as a custom extension, and verify the integration by querying live Bitbucket data from the Goose chat.

Step 1: Configure Bitbucket connectivity for Goose

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

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

2. Select Bitbucket from the Add Connection panel

3. Enter the necessary authentication properties to connect to Bitbucket.

   For most queries, you must set the Workspace. The only exception to this is the Workspaces table, which does not require this property to be set, as querying it provides a list of workspace slugs that can be used to set Workspace. To query this table, you must set Schema to 'Information' and execute the query SELECT * FROM Workspaces>.

   Setting Schema to 'Information' displays general information. To connect to Bitbucket, set these parameters:

   • Schema: To show general information about a workspace, such as its users, repositories, and projects, set this to Information. Otherwise, set this to the schema of the repository or project you are querying. To get a full set of available schemas, query the sys_schemas table.
   • Workspace: Required if you are not querying the Workspaces table. This property is not required for querying the Workspaces table, as that query only returns a list of workspace slugs that can be used to set Workspace.

   Authenticating to Bitbucket

   Bitbucket supports OAuth authentication only. To enable this authentication from all OAuth flows, you must create a custom OAuth application, and set AuthScheme to OAuth.

   Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).

   Creating a custom OAuth application

   From your Bitbucket account:

   1. Go to Settings (the gear icon) and select Workspace Settings.
   2. In the Apps and Features section, select OAuth Consumers.
   3. Click Add Consumer.
   4. Enter a name and description for your custom application.
   5. Set the callback URL:
      • For desktop applications and headless machines, use http://localhost:33333 or another port number of your choice. The URI you set here becomes the CallbackURL property.
      • For web applications, set the callback URL to a trusted redirect URL. This URL is the web location the user returns to with the token that verifies that your application has been granted access.
   6. If you plan to use client credentials to authenticate, you must select This is a private consumer. In the driver, you must set AuthScheme to client.
   7. Select which permissions to give your OAuth application. These determine what data you can read and write with it.
   8. To save the new custom application, click Save.
   9. After the application has been saved, you can select it to view its settings. The application's Key and Secret are displayed. Record these for future use. You will use the Key to set the OAuthClientId and the Secret to set the OAuthClientSecret.
   
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 Goose. 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 Bitbucket connection configured and a PAT generated, Goose can now connect to Bitbucket data through Connect AI.

Step 2: Install and set up Goose

Next, install Goose, choose an LLM provider, and add the Connect AI Remote MCP Server as a custom extension so the agent can discover and call live data tools through Connect AI.

1. Download and install Goose by following the official installation guide, then launch the application
2. On the Welcome to goose screen, choose an AI provider. Select Use Free/Local Providers to run a local model, or Connect to a Provider to configure a provider such as OpenAI, Anthropic, or Google, and enter the required API key
3. In the left navigation, click Extensions, then click + Add custom extension
4. In the extension dialog, configure the server with the following values:
   • Extension Name: CData MCP, or any name of your choice
   • Type: Streamable HTTP
   • Endpoint: https://mcp.cloud.cdata.com/mcp
5. Under Request Headers, add the following two headers and click + Add after each pair:
   • Authorization: Basic your_base64_encoded_email_PAT
   • Content-Type: application/json

   Note: Goose 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==

   
6. Click Save to save the configuration
7. Return to the chat, click the extensions icon at the bottom of the chat input, and confirm that the configured MCP is enabled

With the MCP server added and an LLM provider configured, Goose is ready to query live Bitbucket data through Connect AI.

Step 3: Query live Bitbucket data from Goose

With the integration complete, use the Goose chat to interact with live Bitbucket data through natural language prompts handled by the configured LLM.

1. With the CData MCP extension enabled, type a prompt in the chat, for example:
   • List catalogs from my CData MCP
   • Show the available schemas and tables for Bitbucket
   • Query the top 5 records from a table in Bitbucket data
2. Goose calls the Connect AI MCP Server and returns live results from Bitbucket data

At this point, your Goose agent communicates with the Connect AI MCP Server and retrieves live Bitbucket data through remote MCP tools directly from the chat.

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