Use Azure AI Foundry to Talk to Your Linear Data via CData Connect AI

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

Azure AI Foundry is Microsoft's comprehensive platform for building, deploying, and managing AI applications and agents. It provides a unified environment for creating intelligent agents that can automate tasks, answer questions, and assist with various business processes. When combined with CData Connect AI Remote MCP, you can leverage Azure AI Foundry to interact with your Linear data in real-time. This article outlines the process of connecting to Linear using Connect AI Remote MCP and creating an agent in Azure AI Foundry to interact with your Linear data.

CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to Linear data. The CData Connect AI Remote MCP Server enables secure communication between Azure AI Foundry and Linear. This allows you to ask questions and take actions on your Linear data using Azure AI Foundry agents, 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 Linear. This leverages server-side processing to swiftly deliver the requested Linear data.

In this article, we show how to build an agent in Azure AI Foundry to conversationally explore (or Vibe Query) your data. The connectivity principles apply to any Azure AI Foundry agent. With Connect AI you can build AI agents with access to live Linear data, plus hundreds of other sources.

Step 1: Create an Azure AI Foundry Resource

Before connecting to Linear data, you'll need to create an Azure AI Foundry resource in your Azure portal.

  1. Log into the Azure Portal.
  2. Click Create a resource and search for Microsoft Foundry.
  3. Click Create to begin the resource creation wizard. Creating a Microsoft Foundry resource in Azure Portal
  4. In the Basics tab:
    • Select or create a Resource group
    • Enter a Name for your Foundry resource
    • Enter a Project name
    • Click Next
  5. Configure the Storage, Network, Identity, Encryption, and Tags tabs according to your organization's requirements, clicking Next after each section.
  6. On the Review + submit tab, review your settings and click Create. Review and create Azure AI Foundry resource
  7. Once the resource is created, click Go to resource.
  8. Click Go to Foundry portal to access the Azure AI Foundry portal. Azure AI Foundry portal homepage

Step 2: Configure Linear Connectivity for Azure AI Foundry

Connectivity to Linear from Azure AI Foundry is made possible through CData Connect AI Remote MCP. To interact with Linear data from Azure AI Foundry, we start by creating and configuring a Linear connection in CData Connect AI.

  1. Log into Connect AI, click Connections and click Add Connection Adding a Connection
  2. Select "Linear" from the Add Connection panel Selecting a data source
  3. Enter the necessary authentication properties to connect to Linear.

    You can authenticate to Linear with a personal API key or with OAuth 2.0. The API key is the simplest option for connecting with your own Linear account.

    Authenticating with an API Key

    Set the following connection properties:

    • AuthScheme: Set this to APIKey.
    • APIKey: A Linear personal API key.

    To create a personal API key, log in to Linear, open Settings > Security & access > Personal API keys, select New API key, and create it. Copy the key immediately, because Linear shows it only once.

    Authenticating with OAuth

    OAuth requires a custom OAuth application registered in Linear (Settings > API > OAuth applications), which provides the OAuthClientId and OAuthClientSecret. Two flows are supported:

    • Authorization code: Set AuthScheme to OAuth, InitiateOAuth to GETANDREFRESH, and provide OAuthClientId, OAuthClientSecret, and the CallbackURL defined in your application (e.g., http://localhost:33333). The driver opens Linear in your browser so you can grant access.
    • Client credentials: Set AuthScheme to OAuthClient and provide OAuthClientId and OAuthClientSecret. This authenticates the application itself, with no browser interaction, and suits machine-to-machine integrations.

    By default, the driver requests the read,write scopes. The driver refreshes the access token automatically when it expires.

    Configuring a connection (Salesforce is shown) Click Save & Test
  4. Navigate to the Permissions tab in the Add Linear 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 Azure AI Foundry. 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 Linear data from Azure AI Foundry.

Step 3: Create an AI Agent in Azure AI Foundry

Follow these steps to create an AI agent and connect it to CData Connect AI:

  1. In the Azure AI Foundry portal, click New Foundry to create a new project.

  2. Click Start building and then select Create agent.

  3. Enter a Name for your agent.

    Creating a new agent in Azure AI Foundry

  4. In the Setup section:

    • Choose your preferred AI model
    • Configure Instructions for how the agent should behave

    Configuring agent setup with model and instructions

Step 4: Add the CData Connect AI MCP Tool

Now you'll add the CData Connect AI MCP Server as a custom tool for your agent:

  1. In the agent setup, navigate to the Tools section and click Add.

  2. Select Custom from the tool options.

  3. Choose Model Context Protocol and click Create.

    Selecting Model Context Protocol as the tool type

  4. Enter a Name for the MCP tool (such as "CData Connect AI MCP Server").

  5. In the Remote MCP Server endpoint field, enter: https://mcp.cloud.cdata.com/mcp/

  6. For Authentication, select Key-based.

  7. Configure the credential using:

    • Header name: Authorization
    • Value: Basic EMAIL:PAT, replacing EMAIL with your Connect AI email address and PAT with the personal access token you created earlier
    For example: Basic [email protected]:Uu90pt5vEO...

  8. Click Connect to establish the connection to CData Connect AI.

    Configuring the CData Connect AI MCP Server connection

Optional: Provide Agent Context

You can enhance your agent's understanding by providing specific instructions about using the MCP Server tools. In the agent's Instructions section, you can add guidance such as: 

You are an expert at using the MCP Client tool connected to the CData Connect AI MCP Server. Always search thoroughly and use the most relevant MCP Client tool for each query. Below are the available tools and a description of each:

queryData: Execute SQL queries against connected data sources and retrieve results. When you use the queryData tool, ensure you use the following format for the table name: catalog.schema.tableName
getCatalogs: Retrieve a list of available connections from CData Connect AI. The connection names should be used as catalog names in other tools and in any queries to CData Connect AI. Use the `getSchemas` tool to get a list of available schemas for a specific catalog.
getSchemas: Retrieve a list of available database schemas from CData Connect AI for a specific catalog. Use the `getTables` tool to get a list of available tables for a specific catalog and schema.
getTables: Retrieve a list of available database tables from CData Connect AI for a specific catalog and schema. Use the `getColumns` tool to get a list of available columns for a specific table.
getColumns: Retrieve a list of available database columns from CData Connect AI for a specific catalog, schema, and table.
getProcedures: Retrieve a list of stored procedures from CData Connect AI for a specific catalog and schema
getProcedureParameters: Retrieve a list of stored procedure parameters from CData Connect AI for a specific catalog, schema, and procedure.
executeProcedure: Execute stored procedures with parameters against connected data sources

Step 5: Chat with Your Linear Data

With your agent configured and connected to CData Connect AI, you can now interact with your Linear data using natural language:

  1. In the Azure AI Foundry portal, navigate to the Chat with data section of your agent.

  2. Start asking questions about your Linear data. For example:

    • "Show me all customers from the last 30 days"
    • "What are my top performing products?"
    • "Analyze sales trends for Q4"
    • "List all active projects with their current status"

    Chatting with live data in Azure AI Foundry

  3. The agent will use the CData Connect AI MCP Server to query your Linear data in real-time and provide responses based on live data.

Step 6: Publish Your Agent

Once you're satisfied with your agent's configuration and testing, click Publish to make your agent available for use in your organization.

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