Integrate Goose with Live Linear Data via CData Connect AI

Goose is an open source AI agent that runs locally on your machine and works with a wide range of LLM providers. It supports the model context protocol (MCP) through its extensions framework, so you can add external tools and data sources and give the agent access to live systems beyond the model's training data.

By integrating Goose with CData Connect AI through the built-in MCP Server, Goose gains governed, real-time access to live Linear data. You can list catalogs, explore schemas, and query records from Linear data using natural language prompts, with all data access running securely against authorized sources.

This article explains how to configure Linear connectivity in Connect AI, generate the required personal access token, install and set up Goose, add the Connect AI MCP Server as a custom extension, and verify the integration by querying live Linear data from the Goose chat.

Step 1: Configure Linear connectivity for Goose

Connectivity to Linear from Goose is made possible through Connect AI's Remote MCP Server. To interact with Linear data from Goose, start by creating and configuring a Linear connection in 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 and update user-based permissions

Add a Personal Access Token

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

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

Step 2: Install and set up Goose

Next, install Goose, choose an LLM provider, and add the Connect AI Remote MCP Server as a custom extension so the agent can discover and call live data tools through Connect AI.

1. Download and install Goose by following the official installation guide, then launch the application
2. On the Welcome to goose screen, choose an AI provider. Select Use Free/Local Providers to run a local model, or Connect to a Provider to configure a provider such as OpenAI, Anthropic, or Google, and enter the required API key
3. In the left navigation, click Extensions, then click + Add custom extension
4. In the extension dialog, configure the server with the following values:
   • Extension Name: CData MCP, or any name of your choice
   • Type: Streamable HTTP
   • Endpoint: https://mcp.cloud.cdata.com/mcp
5. Under Request Headers, add the following two headers and click + Add after each pair:
   • Authorization: Basic your_base64_encoded_email_PAT
   • Content-Type: application/json

   Note: Goose will use Basic authentication with Connect AI. Combine your Connect AI user email and the PAT you created earlier in the format email:PAT, base64 encode the combined string, and prefix it with Basic. For example, given [email protected]:ABC123...XYZ789, the Authorization header value becomes something like: Basic dXNlckBkb21haW4uY29tOkFCQzEyMy4uLlhZWjc4OQ==

   
6. Click Save to save the configuration
7. Return to the chat, click the extensions icon at the bottom of the chat input, and confirm that the configured MCP is enabled

With the MCP server added and an LLM provider configured, Goose is ready to query live Linear data through Connect AI.

Step 3: Query live Linear data from Goose

With the integration complete, use the Goose chat to interact with live Linear data through natural language prompts handled by the configured LLM.

1. With the CData MCP extension enabled, type a prompt in the chat, for example:
   • List catalogs from my CData MCP
   • Show the available schemas and tables for Linear
   • Query the top 5 records from a table in Linear data
2. Goose calls the Connect AI MCP Server and returns live results from Linear data

At this point, your Goose agent communicates with the Connect AI MCP Server and retrieves live Linear data through remote MCP tools directly from the chat.

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