Query Live Bitbucket Data in Zed Editor via CData Connect AI

Zed is a high-performance, open-source code editor built for speed and collaboration. Its built-in AI agent panel supports LLM-powered interactions and MCP (Model Context Protocol) tool integrations, enabling developers to access live external data sources directly within the editor.

By integrating Zed with CData Connect AI through the built-in MCP (Model Context Protocol) Server, the Zed AI agent gains governed, real-time access to live Bitbucket data. This enables developers to query schemas, retrieve records, and explore Bitbucket data without leaving the editor or writing custom integration code.

This article explains how to configure Bitbucket connectivity in Connect AI, register the CData MCP Server in Zed, and query live Bitbucket data from the Zed agent panel.

Step 1: Configure Bitbucket connectivity for Zed

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

Step 2: Configure Connect AI in Zed

Now, let's register the CData Connect AI MCP endpoint in Zed so that the built-in AI agent can discover and call live data tools.

1. Download and install Zed
2. Open the agent panel by pressing Ctrl + Shift + /, or by clicking the sparkle icon at the bottom right of the editor
3. In the agent panel, click the ... (toggle agent menu) and select Add Custom Server from the dropdown
4. Select the Configure Remote option to configure CData's MCP
5. An Add MCP Server dialog opens displaying a remote server configuration template. Replace the placeholder content with the following JSON:

   {
       "cdata": {
           "url": "https://mcp.cloud.cdata.com/mcp",
           "headers": {
               "Authorization": "Basic your_base64_encoded_email_PAT"
           }
       }
   }
           

   Note: Combine your Connect AI email and PAT in the format email:PAT, Base64 encode the combined string, and prefix it with Basic. For example, given [email protected]:ABC123...XYZ, the header value becomes something like: Basic dXNlckBteWRvbWFpbjphSzkvbVB4Mi9Rcjd2TjQ...

   
6. Click Add Server or press Ctrl + Enter to register the MCP server

Configure an LLM provider

Zed requires at least one LLM provider to power the agent's reasoning. Configure a provider so the agent can interpret queries and call MCP tools through Connect AI.

1. Click the ... (toggle agent menu) and select Settings
2. Under LLM Providers, expand your preferred provider (e.g., Anthropic, OpenAI, Google AI) and enter your API key
3. Under Model Context Protocol (MCP) Servers, confirm that cdata appears with a green dot and the toggle is enabled

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

Step 3: Query live Bitbucket data from the Zed agent

With the integration complete, use the Zed agent panel to explore and interact with live Bitbucket data through natural language prompts.

1. Open the agent panel using Ctrl + Shift + / and start a new thread
2. Enter a prompt to interact with your data, for example:
   • List all catalogs in my cdata connection
   • Show the available schemas and tables for Bitbucket
   • Query the top 5 records from a table in Bitbucket data
3. The agent calls the CData Connect AI MCP Server and returns live results from Bitbucket data

At this point, your Zed agent communicates with the CData Connect AI MCP Server and retrieves live Bitbucket data through remote MCP tools 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! Start a free 14-day trial of CData Connect AI today, and as always, our world-class Support Team is available to assist you with any questions you may have.