How to Connect to Live Bitbucket Data from Google ADK Agents (via CData Connect AI)

Jerod Johnson
Jerod Johnson
Director, Technology Evangelism
Leverage the CData Connect AI Remote MCP Server to enable Google ADK agents to securely read and take actions on your Bitbucket data for you.

Google ADK (Agent Development Kit) is a powerful, model-agnostic framework for building AI agents that can interact with various data sources and services. When combined with CData Connect AI Remote MCP, you can leverage Google ADK to build intelligent agents that interact with your Bitbucket data in real-time through natural language queries. This article outlines the process of connecting to Bitbucket using Connect AI Remote MCP and configuring a Google ADK agent to interact with your Bitbucket data through ADK Web.

CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to Bitbucket data. The CData Connect AI Remote MCP Server enables secure communication between Google ADK agents and Bitbucket. This allows your agents to read from and take actions on your Bitbucket data, all without the need for data replication to a natively supported database. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported SQL operations, including filters and JOINs, directly to Bitbucket. This leverages server-side processing to swiftly deliver the requested Bitbucket data.

In this article, we show how to configure a Google ADK agent to conversationally explore (or Vibe Query) your data using natural language. With Connect AI you can build agents with access to live Bitbucket data, plus hundreds of other sources.

Step 1: Configure Bitbucket Connectivity for Google ADK

Connectivity to Bitbucket from Google ADK agents is made possible through CData Connect AI Remote MCP. To interact with Bitbucket data from your ADK agent, we 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. Adding a Connection
  2. Select "Bitbucket" from the Add Connection panel
    3. Selecting a data source
  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.
    Configuring a connection (Salesforce is shown)
  4. Click Save & Test
  5. Navigate to the Permissions tab in the Add Bitbucket Connection page and update the User-based permissions. Updating permissions

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from your Google ADK agent. It is best practice to create a separate PAT for each service to maintain granularity of access.

  1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
  2. On the Settings page, go to the Access Tokens section and click Create PAT.
  3. Give the PAT a name and click Create. Creating a new PAT
  4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, we are ready to connect to Bitbucket data from your Google ADK agent.

Step 2: Configure Your Google ADK Agent for CData Connect AI

Follow these steps to configure your Google ADK agent to connect to CData Connect AI. You can use our pre-built agent as a starting point, available at https://github.com/CDataSoftware/adk-mcp-client, or follow the instructions below to create your own.

  1. Ensure you have the Google ADK Python SDK installed. If not, install it using pip: 
    pip install google-genkit google-adk
  2. Create or update your agent's configuration file (typically agent.py) to include the CData Connect AI MCP connection. You'll need to configure the MCP toolset with your Connect AI credentials.
  3. Set up your environment variables or configuration for the MCP server connection. Create a .env file in your project root with the following variables: 
    
MCP_SERVER_URL=https://mcp.cloud.cdata.com/mcp
MCP_USERNAME=YOUR_EMAIL
MCP_PASSWORD=YOUR_PAT
    Replace YOUR_EMAIL with your Connect AI email address and YOUR_PAT with the Personal Access Token created in Step 1.
  4. Configure your agent.py file to use the CData Connect AI MCP Server. Here's an example configuration: 
    
import os
import base64
from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset
from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Get configuration from environment
MCP_SERVER_URL = os.getenv('MCP_SERVER_URL', 'https://mcp.cloud.cdata.com/mcp')
MCP_USERNAME = os.getenv('MCP_USERNAME', '')
MCP_PASSWORD = os.getenv('MCP_PASSWORD', '')

# Create auth header for MCP server
auth_header = {}
if MCP_USERNAME and MCP_PASSWORD:
    credentials = f"{MCP_USERNAME}:{MCP_PASSWORD}"
    auth_header = {"Authorization": f"Basic {base64.b64encode(credentials.encode()).decode()}"}

# Define your agent with CData MCP tools
root_agent = LlmAgent(
    model='gemini-2.0-flash-exp',  # You can use any supported model
    name='data_query_assistant',
    instruction="""You are a data query assistant with access to Bitbucket data through CData Connect AI.
    
    You can help users explore and query their Bitbucket data in real-time.
    Use the available MCP tools to:
    - List available databases and schemas
    - Explore table structures
    - Execute SQL queries
    - Provide insights about the data
    
    Always explain what you're doing and format results clearly.""",
    
    tools=[
        MCPToolset(
            connection_params=StreamableHTTPConnectionParams(
                url=MCP_SERVER_URL,
                headers=auth_header
            )
        )
    ],
)
  5. Run your agent with ADK Web. From your project directory, execute: 
    adk web --port 5000 .

    Note: If you installed ADK with pip install --user, the adk command may not be in your PATH. You can either:

    • Use the full path: ~/Library/Python/3.x/bin/adk (on macOS)
    • Add to PATH: export PATH="$HOME/Library/Python/3.x/bin:$PATH"
    • Use a virtual environment where the PATH is automatically configured
  6. Open the ADK Web interface in your browser (typically http://localhost:5000).
  7. Select your agent from the dropdown menu (it will be named based on the name parameter in your agent configuration).
  8. Start interacting with your Bitbucket data through natural language queries. Your agent now has access to your Bitbucket data through the CData Connect AI MCP Server.

Step 3: Build Intelligent Agents with Live Bitbucket Data Access

With your Google ADK agent configured and connected to CData Connect AI, you can now build sophisticated agents that interact with your Bitbucket data using natural language. The MCP integration provides your agents with powerful data access capabilities.

Available MCP Tools for Your Agent

Your Google ADK agent has access to the following CData Connect AI MCP tools:

  • queryData: Execute SQL queries against connected data sources and retrieve results
  • getCatalogs: Retrieve a list of available connections from CData Connect AI
  • getSchemas: Retrieve database schemas for a specific catalog
  • getTables: Retrieve database tables for a specific catalog and schema
  • getColumns: Retrieve column metadata for a specific table
  • getProcedures: Retrieve stored procedures for a specific catalog and schema
  • getProcedureParameters: Retrieve parameter metadata for stored procedures
  • executeProcedure: Execute stored procedures with parameters

Example Use Cases

Here are some examples of what your Google ADK agents can do with live Bitbucket data access:

  • Data Analysis Agent: Build an agent that analyzes trends, patterns, and anomalies in your Bitbucket data
  • Report Generation Agent: Create agents that generate custom reports based on natural language requests
  • Data Quality Agent: Develop agents that monitor and validate data quality in real-time
  • Business Intelligence Agent: Build agents that answer complex business questions by querying multiple data sources
  • Automated Workflow Agent: Create agents that trigger actions based on data conditions in Bitbucket

Testing Your Agent

Once deployed to ADK Web, you can interact with your agent through natural language queries. For example:

  • "Show me all customers from the last 30 days"
  • "What are the top performing products this quarter?"
  • "Analyze sales trends and identify anomalies"
  • "Generate a summary report of active projects"
  • "Find all records that match specific criteria"

Your Google ADK agent will automatically translate these natural language queries into appropriate SQL queries and execute them against your Bitbucket data through the CData Connect AI MCP Server, providing real-time insights without requiring users to write complex SQL or understand the underlying data structure.

Get CData Connect AI

To get live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your Google ADK agents and cloud applications, try CData Connect AI today!

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