How to Connect to Live Linear Data from Gemini CLI (via CData Connect AI)

Gemini CLI is a command-line interface tool that provides direct access to Google's Gemini AI models for code generation, text analysis, and conversational AI capabilities. When combined with CData Connect AI Remote MCP, you can leverage Gemini CLI to interact with your Linear data in real-time through natural language queries. This article outlines the process of connecting to Linear using Connect AI Remote MCP and configuring Gemini CLI 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 Gemini CLI and Linear. This allows you to ask questions and take actions on your Linear data using natural language through Gemini CLI, 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 configure Gemini CLI to conversationally explore (or Vibe Query) your data using natural language. With Connect AI you can query and interact with live Linear data, plus hundreds of other sources.

Step 1: Configure Linear Connectivity for Gemini CLI

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

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Linear" from the Add Connection panel

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.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Linear Connection page and update the User-based permissions.

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Gemini CLI. 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.
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 Gemini CLI.

Step 2: Configure Gemini CLI for CData Connect AI

Follow these steps to configure Gemini CLI to connect to CData Connect AI:

1. Ensure Gemini CLI is installed on your system. If not, install it using npm:

   npm install -g @google-gemini/cli

2. Locate your Gemini CLI settings file. If the file doesn't exist, create it:
   • Linux/Unix/Mac: ~/.gemini/settings.json
   • Windows: %USERPROFILE%\.gemini\settings.json
3. Add the CData Connect AI Remote MCP Server to the mcpServers object in your settings file. Replace YOUR_EMAIL and YOUR_PAT with your Connect AI email address and the PAT created previously:

   
   {
     "mcpServers": {
       "cdata-connect-ai": {
         "httpUrl": "https://mcp.cloud.cdata.com/mcp",
         "headers": {
           "Authorization": "Basic YOUR_EMAIL:YOUR_PAT"
         }
       }
     }
   }    

   For example, if your email is [email protected] and your PAT is Uu90pt5vEO..., the Authorization header would be:

   "Authorization": "Basic [email protected]:Uu90pt5vEO..."

4. Save the settings file. Gemini CLI will now use the CData Connect AI MCP Server for data operations.

Step 3: Query Live Linear Data with Natural Language

With Gemini CLI configured and connected to CData Connect AI, you can now interact with your Linear data using natural language queries. The MCP integration allows you to ask questions and receive responses from the Linear data source in real-time.

Start using Gemini CLI to explore your data:

1. Open your terminal and start a Gemini CLI session:

   gemini

2. You can now use natural language to query 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"
3. Gemini CLI will automatically translate your natural language queries into appropriate SQL queries and execute them against your Linear data through the CData Connect AI MCP Server.

The combination of Gemini CLI's natural language processing capabilities and CData Connect AI's robust data connectivity enables you to explore and analyze your Linear data without writing complex SQL queries or needing deep technical knowledge of the underlying data structure.

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