Integrate Open WebUI with Live Bitbucket Data via CData Connect AI

Open WebUI is an open-source, self-hosted AI chat platform that brings together hosted LLM APIs and locally served models under a single, customizable interface. It supports the Model Context Protocol (MCP) through its tool server framework, allowing you to configure external tools and data sources so that chats can reach beyond the model's training data and interact with live systems.

By integrating Open WebUI with CData Connect AI through the built-in MCP Server, Open WebUI gains governed, real-time access to live Bitbucket data. This enables users to 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 Open WebUI, register the Connect AI MCP Server, configure an LLM provider, and verify the integration by querying live Bitbucket data from the Open WebUI chat interface.

Step 1: Configure Bitbucket connectivity for Open WebUI

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

Step 2: Install Open WebUI and configure Connect AI MCP

Next, install Open WebUI locally and configure the Connect AI Remote MCP Server as a tool server so that the chat interface can discover and call live data tools through Connect AI.

1. Install Open WebUI by following the official Quick Start guide
2. Once the installation is complete, start Open WebUI and open it in your browser to access the chat interface
3. Click your profile icon in the top right and select Admin Panel
4. In the top navigation bar of the Admin Panel, click Settings
5. Select Integrations from the left menu, then click the icon next to Manage Tool Servers to add a new connection
6. In the Add Connection panel, configure the server with the following values:
   • Type: MCP Streamable HTTP
   • Name: CData MCP, or any name of your choice
   • ID: cdata-mcp
   • URL: https://mcp.cloud.cdata.com/mcp
   • Auth: None
7. Expand the Advanced section and paste the following JSON into the Headers field:

   {
       "Authorization": "Basic your_base64_encoded_email_PAT",
       "Content-Type": "application/json"
   }
   		

   Note: Open WebUI 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==

   
8. Click Save to register the tool server

Enable the MCP server and configure an LLM provider

Open WebUI requires at least one LLM provider to power the chat. Enable the MCP server from the chat input and configure an API key for your preferred provider so the model can interpret prompts and call MCP tools through Connect AI.

1. Return to the chat interface, click the Integrations icon at the bottom of the chat input, select Tools, and toggle CData MCP on to expose the tools to the chat
2. At the top of the chat, click the model selector and choose your preferred LLM provider and model. Add your provider API key when prompted so the model can be used in the chat

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

Step 3: Query live Bitbucket data from Open WebUI

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

1. With the CData MCP server enabled and a model selected, type a prompt in the chat input, for example:
   • List all catalogs in my cdata mcp
   • Show the available schemas and tables for Bitbucket
   • Query the top 5 records from a table in Bitbucket data
2. Open WebUI calls the Connect AI MCP Server and returns live results from Bitbucket data

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

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