Integrating LangChain with Jira Service Management Data via CData Connect AI

Dibyendu Datta
Lead Technology Evangelist

Leverage the CData Connect AI Remote MCP Server to enable LangChain agents to securely access and act on Jira Service Management data within automated workflows.

LangChain is a framework used by developers, data engineers, and AI practitioners for building AI-powered applications and workflows by combining reasoning models (LLMs), tools, APIs, and data connectors. By integrating LangChain with CData Connect AI through the built-in MCP Server, workflows can effortlessly access and interact with live Jira Service Management data in real time.

CData Connect AI offers a secure, low-code environment to connect Jira Service Management and other data sources, removing the need for complex ETL and enabling seamless automation across business applications with live data.

This article outlines how to configure Jira Service Management connectivity in CData Connect AI, register the MCP server with LangChain, and build a workflow that queries Jira Service Management data in real time.

Prerequisites

An account in CData Connect AI
Python version 3.10 or higher, to install the LangChain and LangGraph packages
Generate and save an OpenAI API key
Install Visual Studio Code in your system

Step 1: Configure Jira Service Management Connectivity for LangChain

Before LangChain can access Jira Service Management, a Jira Service Management connection must be created in CData Connect AI. This connection is then exposed to LangChain through the remote MCP server.

Log in to Connect AI click Sources, and then click + Add Connection
From the available data sources, choose Jira Service Management
Enter the necessary authentication properties to connect to Jira Service Management
You can establish a connection to any Jira Service Desk Cloud account or Server instance.

Connecting with a Cloud Account

To connect to a Cloud account, you'll first need to retrieve an APIToken. To generate one, log in to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.
Supply the following to connect to data:
- User: Set this to the username of the authenticating user.
- APIToken: Set this to the API token found previously.
Connecting with a Service Account

To authenticate with a service account, supply the following connection properties:
- User: Set this to the username of the authenticating user.
- Password: Set this to the password of the authenticating user.
- URL: Set this to the URL associated with your JIRA Service Desk endpoint. For example, https://yoursitename.atlassian.net.
Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.

Accessing Custom Fields

By default, the connector only surfaces system fields. To access the custom fields for Issues, set IncludeCustomFields.
Click Save & Test
Once authenticated, open the Permissions tab in the Jira Service Management connection and configure user-based permissions as required

Generate a Personal Access Token (PAT)

LangChain authenticates to Connect AI using an account email and a Personal Access Token (PAT). Creating separate PATs for each integration is recommended to maintain access control granularity.

In Connect AI, select the Gear icon in the top-right to open Settings
Under Access Tokens, select Create PAT
Provide a descriptive name for the token and select Create
Copy the token and store it securely. The PAT will only be visible during creation

With the Jira Service Management connection configured and a PAT generated, LangChain is prepared to connect to Jira Service Management data through the CData MCP server.

Note: You can also generate a PAT from LangChain in the Integrations section of Connect AI. Simply click Connect --> Create PAT to generate it.

Navigate to the LangChain integration tool and click on Connect.

Step 2: Connect to the MCP server in LangChain

To connect LangChain with CData Connect AI Remote MCP Server and use OpenAI (ChatGPT) for reasoning, you need to configure your MCP server endpoint and authentication values in a config.py file. These values allow LangChain to call the MCP server tools, while OpenAI handles the natural language reasoning.

Create a folder for LangChain MCP
Create two Python files within the folder: config.py and langchain.py
In config.py, create a class Config to define your MCP server authentication and URL. You need to provide your Base64-encoded CData Connect AI username and PAT (obtained in the prerequisites):
```
 
class Config: 

      MCP_BASE_URL = "https://mcp.cloud.cdata.com/mcp"   #MCP Server URL 
      MCP_AUTH = "base64encoded(EMAIL:PAT)"   #Base64 encoded Connect AI Email:PAT 
```
Note: You can create the base64 encoded version of MCP_AUTH using any Base64 encoding tool.

In langchain.py, set up your MCP server and MCP client to call the tools and prompts:

 
""" 
Integrates a LangChain ReAct agent with CData Connect AI MCP server. 
The script demonstrates fetching, filtering, and using tools with an LLM for agent-based reasoning. 
""" 

import asyncio 
from langchain_mcp_adapters.client import MultiServerMCPClient 
from langchain_openai import ChatOpenAI 
from langgraph.prebuilt import create_react_agent 
from config import Config 

async def main(): 

# Initialize MCP client with one or more server URLs
mcp_client = MultiServerMCPClient( 
  connections={ 
    "default": { # you can name this anything 
    "transport": "streamable_http", 
    "url": Config.MCP_BASE_URL, 
    "headers": {"Authorization": f"Basic {Config.MCP_AUTH}"}, 
    } 
  } 
) 

# Load remote MCP tools exposed by the server 
all_mcp_tools = await mcp_client.get_tools() 
print("Discovered MCP tools:", [tool.name for tool in all_mcp_tools]) 

# Create and run the ReAct style agent 
llm = ChatOpenAI( 
  model="gpt-4o",  
  temperature=0.2, 
  api_key="YOUR_OPEN_API_KEY" #Use your OpenAI API Key here, this can be found here: https://platform.openai.com/ 
) 

agent = create_react_agent(llm, all_mcp_tools) 

user_prompt = "How many tables are available in JiraServiceDesk1?" #Change prompts as per need 
print(f"
User prompt: {user_prompt}") 

# Send a prompt asking the agent to use the MCP tools 
response = await agent.ainvoke( 
  { "messages": [{ "role": "user", "content": (user_prompt),}]} 
) 

# Print out the agent's final response 
final_msg = response["messages"][-1].content 
print("Agent final response:", final_msg) 

if __name__ == "__main__": 

asyncio.run(main())

Step 3: Install the LangChain and LangGraph packages

Since this workflow uses LangChain together with CData Connect AI MCP and integrates OpenAI for reasoning, you need to install the required Python packages.

Run the following command in your project terminal:

 
pip install langchain-mcp-adapters langchain-openai langgraph

Step 4: Prompt Jira Service Management using LangChain (via the MCP server)

When the installation finishes, run python langchain.py to execute the script
The script connects to the MCP server and discovers the CData Connect AI MCP tools available for querying your connected data
Supply a prompt (e.g., "How many tables are available in Jira Service Management?")
Accordingly, the agent responds with the results

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Integrating LangChain with Jira Service Management Data via CData Connect AI

Prerequisites

Step 1: Configure Jira Service Management Connectivity for LangChain

Connecting with a Cloud Account

Connecting with a Service Account

Accessing Custom Fields