Integrate Devin with Live RSS Data Using CData Connect AI

Anusha M B
Anusha M B
Technical Marketing Engineer
Use the CData Connect AI Remote MCP Server to enable Devin, the AI software engineer, to securely access and query live RSS feeds.

Devin is an AI software engineer from Cognition that plans, writes, and tests code in autonomous sessions. It supports the model context protocol (MCP) through its built-in MCP marketplace, allowing you to install remote MCP servers and give Devin access to external tools and live data sources.

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

This article explains how to configure RSS connectivity in Connect AI, install the CData Connect AI MCP Server from the Devin MCP marketplace, and verify the integration by querying live RSS feeds from a Devin session.

Step 1: Configure RSS connectivity for Devin

Connectivity to RSS from Devin is made possible through Connect AI's Remote MCP Server. To interact with RSS feeds from Devin, start by creating and configuring a RSS connection in Connect AI.

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

    You can connect to RSS and Atom feeds, as well as feeds with custom extensions. To connect to a feed, set the URL property. You can also access secure feeds. A variety of authentication mechanisms are supported. See the help documentation for details.

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

With the RSS connection configured, Devin can now connect to RSS feeds through Connect AI.

Step 2: Install the Connect AI MCP Server in Devin

Next, install the CData Connect AI MCP Server from the Devin MCP servers list so your sessions can discover and call live data tools through Connect AI.

Note: Installing MCP servers in Devin requires the Manage MCP Servers permission. If you don't have this permission, use the Suggest option or contact your organization admin.

  1. Log into Devin and navigate to Settings and Connections, then select the MCP servers tab
  2. Search for CData Connect AI in the marketplace and select the verified listing
  3. Viewing MCP servers in Devin and searching for CData Connect AI
  4. Click Install and enable. When prompted, complete the OAuth flow to authenticate with your Connect AI account Installing the Connect AI MCP Server in Devin
  5. Once installed, confirm the Enable in sessions toggle is on and click Test tools. Devin validates the connection and lists the available Connect AI tools, such as getCatalogs, getSchemas, getTables, and queryData Verifying the Connect AI MCP tools in Devin

With the MCP server installed and enabled, the Connect AI tools are available to every Devin session in your workspace.

Step 3: Query live RSS feeds from Devin

With the integration complete, start a Devin session and interact with live RSS feeds through natural language prompts.

  1. From the Devin home page, start a new session
  2. In the session prompt, ask Devin to work with your Connect AI data, for example:
    • Can you list the connections in CData Connect AI
    • Show the available schemas and tables for RSS
    • Query the top 5 records from a table in RSS
  3. Devin calls the Connect AI MCP Server and returns live results from RSS feeds directly in the session. You can follow the tool calls, inputs, and outputs in the Progress panel Querying live data from a Devin session

At this point, Devin communicates with the Connect AI MCP Server and retrieves live RSS feeds through remote MCP tools directly from your sessions, whether you're exploring data, building features against it, or automating engineering tasks.

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