Integrate Live Bitbucket Data in the Windsurf IDE via CData Connect AI

Windsurf is an AI-native IDE built around Cascade, an autonomous coding agent that understands project context and executes multi-step tasks directly inside the editor. Cascade supports the Model Context Protocol (MCP), allowing the agent to discover and call external tools and data sources without leaving the development environment.

By integrating Windsurf with CData Connect AI through the built-in MCP server, the Cascade agent gains governed, real-time access to live Bitbucket data. This enables developers to list catalogs, inspect schemas, and query records from Bitbucket data within the IDE using natural language prompts.

This article explains how to configure Bitbucket connectivity in Connect AI, generate the required personal access token, configure the Connect AI MCP Server in Windsurf, and verify the integration by querying live Bitbucket data from the Cascade chat.

Step 1: Configure Bitbucket connectivity for Windsurf

Connectivity to Bitbucket from Windsurf is made possible through Connect AI's Remote MCP Server. To interact with Bitbucket data from Windsurf, 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 Windsurf. 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, Windsurf can now connect to Bitbucket data.

Step 2: Configure Connect AI MCP in Windsurf

Next, configure the Connect AI Remote MCP Server in Windsurf so that the Cascade agent can discover and call live data tools through Connect AI.

1. Download and install the Windsurf IDE
2. Open Windsurf, click your profile icon in the top right, and select Windsurf Settings
3. Under the Cascade section, locate MCP Servers and click Open MCP Registry
4. In the MCP Marketplace, click Add custom MCP in the top right
5. This opens the mcp_config.json file. Paste the following JSON:

   {
       "mcpServers": {
           "cdata-mcp": {
               "serverUrl": "https://mcp.cloud.cdata.com/mcp",
               "headers": {
                   "Authorization": "Basic your_base64_encoded_email_PAT",
                   "Content-Type": "application/json"
               }
           }
       }
   }
   		

   Note: Windsurf 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. Save the mcp_config.json file and return to the MCP Registry
7. Under Installed, confirm that cdata-mcp is listed and marked as Enabled

With the MCP server registered and enabled, Windsurf is ready to query live Bitbucket data through Connect AI.

Step 3: Query live Bitbucket data from Windsurf

With the integration complete, use the Cascade chat panel in Windsurf to interact with live Bitbucket data through natural language prompts.

1. On the top bar of Windsurf, switch from Editor to Agent to open a new Cascade chat
2. At the bottom of the chat panel, confirm that the cdata-mcp server is listed and the toggle is enabled
3. Start interacting with the agent by entering prompts like:
   • List all catalogs in my cdata-mcp connection
   • Show the available schemas and tables for Bitbucket
   • Query the top 5 records from a table in Bitbucket data
4. The Cascade agent calls the Connect AI MCP Server and returns live results from Bitbucket data

At this point, your Windsurf IDE communicates with the Connect AI MCP Server and retrieves live Bitbucket data through remote MCP directly from the editor.

Get 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 today, and as always, our Support Team is available to assist you with any questions you may have.