Use Grok to Talk to Your Bitbucket Data via CData Connect AI

Grok AI is a large language model developed by xAI for real-time reasoning, tool invocation, and agentic workflows. It enables developers to build AI agents that can reason over live data, discover tools dynamically, and take intelligent actions.

CData Connect AI provides a secure cloud-to-cloud interface for integrating hundreds of enterprise data sources with Grok AI. Using Connect AI, live Bitbucket data is exposed through a remote MCP endpoint without replication, allowing Grok AI agents to securely query and analyze governed enterprise data in real time.

Step 1: Configure Bitbucket in CData Connect AI

To enable Grok to query live Bitbucket data, first create a Bitbucket connection in CData Connect AI. This connection is exposed through the CData Remote MCP Server.

1. Log into Connect AI, click Sources, and then click Add Connection.
2. Select "Bitbucket" from the Add Connection panel.
3. Enter the required authentication properties.

   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.
   Click Create & Test.
4. Open the Permissions tab and configure user access.

Add a Personal Access Token

A Personal Access Token (PAT) authenticates MCP requests from Agno to CData Connect AI.

1. Open Settings and navigate to Access Tokens.
2. Click Create PAT.
3. Save the generated token securely.

Step 2: Install required dependencies

Remote MCP Tools allow Grok to connect to external MCP (Model Context Protocol) servers, extending its capabilities with custom tools from third parties or your own implementations. Simply specify a server URL and optional configuration xAI manages the MCP server connection and interaction on your behalf.

Open the terminal and install the required dependencies for the MCP integration using pip.

	pip install xai-sdk==1.4.0

The xai-sdk (v1.4.0) enables Remote MCP tools, and python-dotenv is used to securely load environment variables.

	pip install python-dotenv

Step 3: Generate an xAI API key

1. Create or login to xAI account
2. Open xAI API console
3. Navigate to API Keys

4. Click on create API key

After generating an API key, user need to save it somewhere safe. Recommended option is to export it as an environment variable in your terminal or save it to a .env file.

Step 4: Connect to CData Connect AI

Initialize the Grok client and configure the MCP connection to CData Connect AI. The code below establishes a secure connection and sends a natural language query to your data source.

import os

from xai_sdk import Client
from xai_sdk.chat import user
from xai_sdk.tools import mcp

client = Client(api_key="Your_xAI-API_KEY")
chat = client.chat.create(
	model="grok-4-1-fast-non-reasoning",
	tools=[
		mcp(
			server_url="https://mcp.cloud.cdata.com/mcp",
			extra_headers={"Authorization": "Basic Username:PAT"} #Base64 Encoded Username:PAT
		)
		],
	include=["verbose_streaming"],
)

chat.append(user("List the top two catalogs for me please"))

is_thinking = True
for response, chunk in chat.stream():
	# View the server-side tool calls as they are being made in real-time
	for tool_call in chunk.tool_calls:
		print(f"
Calling tool: {tool_call.function.name} with arguments: {tool_call.function.arguments}")
	if response.usage.reasoning_tokens and is_thinking:
		print(f"
Thinking... ({response.usage.reasoning_tokens} tokens)", end="", flush=True)
	if chunk.content and is_thinking:
		print("

Final Response:")
		is_thinking = False
	if chunk.content and not is_thinking:
		print(chunk.content, end="", flush=True)

print("

Usage:")
print(response.usage)
print(response.server_side_tool_usage)
print("

Server Side Tool Calls:")
print(response.tool_calls)

This code initializes the Grok AI client, connects to CData Connect AI via MCP using Basic Authentication, and streams the response in real-time. The agent automatically discovers available tools, invokes them to query your live data, and displays both the tool calls and final results.

Run the script to see Grok query your connected data source.

Query Results

The output shows Grok invoking MCP tools through CData Connect AI and returning live data from your connected source.

User can now query live data using natural language through Grok AI.

Build agentic workflows with Grok and CData Connect AI

Combining Grok AI with CData Connect AI delivers AI-powered data access without pipelines or custom integrations. Start your free trial today to see how CData can empower Grok with live, secure access to hundreds of external systems.