How to Use the CData Connect Cloud MCP in Claude Code

Q: How do I set up CData Connect Cloud with Claude Code?

Run claude mcp add --transport http cdata -- in your terminal. Once added, you can verify it's connected with claude mcp list. Claude Code will then be able to use all the CData tools.

Q: How do I pipe query results to other tools with CData Connect Cloud and Claude Code?

When you use the cdataexecutequery tool, Claude Code prints the structured output (usually JSON) to stdout. You can then pipe this directly to another program, like claude code ... jq '. .email' to extract a list of emails.

Q: What's the best way to run a health check on my databases using this?

The cdatatestconnection tool is designed for this. You can create a simple cron job or script that periodically calls this tool for each of your critical connections. If it fails, you can trigger an alert.

Pipe any data source directly into your terminal scripts. Query databases and APIs with CData, right from the command line with Claude Code.

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Create your Vinkius account to connect CData Connect Cloud to Claude Code and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup CData Connect Cloud with Claude Code

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Run SQL in Your Shell Scripts

The `cdata_execute_query` tool lets you run SQL against any configured CData source and get the output directly in your terminal. Pipe the results to `jq`, `awk`, or any other command-line tool. It's perfect for quick data pulls, reporting, or ad-hoc analysis without an IDE. Forget about writing one-off Python scripts just to connect to a database. You can now build powerful one-liners. List your connections with `cdata_list_connections`, find the right table with `cdata_list_tables`, and pull the data you need in seconds.

Automate Data Ops in CI/CD

This MCP Server is built for automation. Use the CData tools in your CI/CD pipelines for tasks like database health checks or data validation. Before deploying, have your script run `cdata_test_connection` to ensure a critical data source is online. You can also manage connections programmatically. A setup script can use `cdata_create_connection` to provision a new data source for a testing environment and tear it down afterward. It's all headless and designed for unattended execution.

Headless Schema and Workspace Management

Manage your entire CData setup from the command line. The `cdata_list_workspaces` tool lets you see all your organization's data groups. From there, you can drill down into schemas using `cdata_get_schema_metadata` and `cdata_get_table_columns`. This is great for scripting administrative tasks. You can write a shell script to audit all tables across all connections, check for specific column names, or generate documentation based on live schema data. Claude Code makes it simple to automate these chores.

Setup guide

Set up CData Connect Cloud MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see cdata-connect-cloud-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest CData Connect Cloud transactions." It will automatically discover and invoke the available CData Connect Cloud tools.

Terminal

claude mcp add --transport http cdata-connect-cloud-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

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Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the CData Connect Cloud MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

{
  "mcpServers": {
    "cdata-connect-cloud-mcp": {
      "type": "url",
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CData Connect. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about CData Connect Cloud MCP in Claude Code

Run `claude mcp add --transport http cdata -- ` in your terminal. Once added, you can verify it's connected with `claude mcp list`. Claude Code will then be able to use all the CData tools.

Absolutely. You can add the setup command to your workflow file to connect Claude Code to the CData Connect Cloud MCP Server. Then you can call the tools in your script to run queries, check connections, or validate data as part of your CI pipeline.

When you use the `cdata_execute_query` tool, Claude Code prints the structured output (usually JSON) to stdout. You can then pipe this directly to another program, like `claude code ... | jq '.[] | .email'` to extract a list of emails.

The `cdata_test_connection` tool is designed for this. You can create a simple cron job or script that periodically calls this tool for each of your critical connections. If it fails, you can trigger an alert.

The Vinkius platform secures the entire process. Your query is sent over a secure transport to the MCP Server, which runs in a sandboxed environment. The server uses your CData credentials to execute the query and streams the result back. Neither the credentials nor the query data are ever logged or stored on disk.

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