Integrate Trae with Live Bitbucket Data via CData Connect AI

Trae is an AI-powered integrated development environment (IDE) that pairs a familiar editor with agent modes such as Builder and SOLO. It supports the Model Context Protocol (MCP), so you can add external tools and data sources and give the agent access to live data.

By integrating Trae with CData Connect AI through the built-in MCP Server, Trae 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 Trae, add the Connect AI MCP Server, configure an LLM model, and verify the integration by querying live Bitbucket data from the Trae agent.

Step 1: Configure Bitbucket connectivity for Trae

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

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

Next, install Trae, add the Connect AI Remote MCP Server, and configure an LLM model so the agent can discover and call live data tools through Connect AI.

1. Download and install the Trae IDE, then launch the application
2. Switch to SOLO mode using the toggle at the top left, or press Ctrl + Alt + \
3. Click Toggle AI Sidebar to open the chat panel
4. Open Settings, then select MCP from the left menu
5. Click Add Manually
6. In the Configure Manually dialog, paste the following configuration and click Confirm:

   {
     "mcpServers": {
       "cdata-connect-ai": {
         "type": "streamable-http",
         "url": "https://mcp.cloud.cdata.com/mcp",
         "headers": {
           "Authorization": "Basic your_base64_encoded_email_PAT"
         }
       }
     }
   }
   		

   Note: Trae 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==

   

Configure an LLM model

Trae requires at least one LLM model to power the agent's reasoning. Add a model so the agent can interpret prompts and call MCP tools through Connect AI.

1. Return to Settings and select Models
2. Click Add Model, choose a provider such as OpenAI, Anthropic, or Google, select a model, enter your API key, and click Add Model

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

Step 3: Query live Bitbucket data from Trae

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

1. In the chat panel, type @ and select Builder with MCP. Confirm that cdata-connect-ai is listed under Tools - MCP
2. Enter a prompt to interact with your data, for example:
   • List all catalogs in cdata-connect-ai
   • Show the available schemas and tables for Bitbucket
   • Query the top 5 records from a table in Bitbucket data
3. Trae calls the Connect AI MCP Server and returns live results from Bitbucket data

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

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