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

Anusha M B
Anusha M B
Technical Marketing Engineer
Leverage the CData Connect AI Remote MCP Server to enable Grok AI to securely answer questions and take actions on your Jira data for you.

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 350+ enterprise data sources with Grok AI. Using Connect AI, live Jira 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.

About Jira Data Integration

CData simplifies access and integration of live Jira data. Our customers leverage CData connectivity to:

  • Gain bi-directional access to their Jira objects like issues, projects, and workflows.
  • Use SQL stored procedures to perform functional actions like changing issues status, creating custom fields, download or uploading an attachment, modifying or retrieving time tracking settings, and more.
  • Authenticate securely using a variety of methods, including username and password, OAuth, personal access token, API token, Crowd or OKTA SSO, LDAP, and more.

Most users leverage CData solutions to integrate Jira data with their database or data warehouse, whether that's using CData Sync directly or relying on CData's compatibility with platforms like SSIS or Azure Data Factory. Others are looking to get analytics and reporting on live Jira data from preferred analytics tools like Tableau and Power BI.

Learn more about how customers are seamlessly connecting to their Jira data to solve business problems from our blog: Drivers in Focus: Collaboration Tools.


Getting Started


Step 1: Configure Jira in CData Connect AI

To enable Grok to query live Jira data, first create a Jira 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 "Jira" from the Add Connection panel.
  3. Enter the required authentication properties.

    To connect to JIRA, provide the User and Password. Additionally, provide the Url; for example, https://yoursitename.atlassian.net.

    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 350+ external systems.

Ready to get started?

Learn more about CData Connect AI or sign up for free trial access:

Free Trial