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⇱ Build Agents in Relevance AI with Access to Live Jira Data via CData Connect AI

Build Agents in Relevance AI with Access to Live Jira Data via CData Connect AI

👁 Yazhini G
Yazhini G
Technical Marketing Engineer
Leverage the CData Connect AI Remote MCP Server to enable Relevance AI to securely access and act on Jira data within intelligent agent workflows.

Relevance AI is an AI automation and agent-building platform that enables organizations to create autonomous workflows powered by natural language reasoning. Users can visually design agents that interact with APIs, databases, and third-party systems to complete everyday business tasks or data operations.

By integrating Relevance AI with CData Connect AI through the built-in MCP (Model Context Protocol) Server, your agents can query, summarize, and act on live Jira data in real time. This connection bridges Relevance AI intelligent workflow engine with the governed enterprise connectivity of CData Connect AI ensuring every query runs securely against authorized sources without manual data export.

This article outlines the steps to configure Jira connectivity in Connect AI, register Connect AI in Relevance AI, and build an agent that interacts with live Jira data.

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 Connectivity for Relevance AI

Connectivity to Jira from Relevance AI is made possible through CData Connect AI's Remote MCP Server. To interact with Jira data from Relevance AI, we start by creating and configuring a Jira connection in CData Connect AI.

  1. Log into Connect AI, click Sources, and then click Add Connection
    2. 👁 Adding a connection in Connect AI
  2. Select Jira from the Add Connection panel
    3. 👁 Selecting data source
  3. Enter the necessary authentication properties to connect to Jira.

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

    👁 Configuring a connection (Salesforce is shown)
  4. Click Save & Test
  5. Navigate to the Permissions tab and update user-based permissions
    6. 👁 Updating permissions

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Relevance AI. 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. 👁 Creating a new PAT
  4. Copy the token when displayed and store it securely. It will not be shown again

With the Jira connection configured and a PAT generated, Relevance AI can now connect to Jira data through Connect AI.

Step 2: Configure Connectivity in Relevance AI

The CData Connect AI MCP endpoint and authorization details are registered within Relevance AI so that agents can call live data from Connect AI.

  1. Sign in to Relevance AI and create an account if you do not already have one
  2. From the sidebar, navigate to Agents and then click on New Agent
    3. 👁 Creating a new agent in Relevance AI
  3. Select Build from scratch and name the agent (eg; CData MCP Server)
    4. 👁 Building an agent from scratch
  4. Inside the agent editor, select Advanced and then switch to the MCP Server tab
    5. 👁 Opening MCP Server settings
  5. Click + Add Remote MCP Tools
  6. In the dialog that appears, fill out the fields as follows:
    • URL: https://mcp.cloud.cdata.com/mcp
    • Label: Any custom label (eg; cdata_mcp_server)
    • Authentication: Select Custom headers
    • Add header key:value pair. Combine your email and PAT as email:PAT and encode that string in Base64 and then prefix with the word Basic
      • Key: Authorization
      • Value: Basic base64(email:PAT)
    👁 Connecting to CData Connect AI MCP Server in Relevance AI

Click Connect to establish the connection. Relevance AI will verify your credentials and register the CData Connect AI MCP Server for use in agents.

Step 3: Build and Run a Relevance AI Agent with Live Jira Data

  1. Switch to the Run tab for your agent
  2. Enter a task for example, "List the five most recent incidents from ServiceNow"
    3. 👁 Running the Relevance AI agent
  3. The agent will query Connect AI via the MCP endpoint and display live results from Jira data
    4. 👁 Example query result from Connect AI

With the connection complete, Relevance AI agents can now issue queries, retrieve records, and perform AI-driven tasks over live Jira data through CData Connect AI MCP Server.

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