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Cursor is an AI-powered code editor that integrates agentic AI into everyday development workflows. With support for MCP, Cursor can connect to local tools and enterprise data sources directly from the editor, enabling natural language interaction with live systems without switching context.
Model Context Protocol (MCP) is an open standard for connecting LLM clients to external services through structured tool interfaces. MCP servers expose capabilities such as schema discovery and live querying, allowing AI agents to retrieve and reason over real-time data safely and consistently.
In this article, we guide you through installing the CData Code Assist MCP for Google BigQuery, configuring the connection to BigQuery, connecting the Code Assist MCP add-on to Cursor, and querying live BigQuery data from within the editor.
CData simplifies access and integration of live Google BigQuery data. Our customers leverage CData connectivity to:
Most CData customers are using Google BigQuery as their data warehouse and so use CData solutions to migrate business data from separate sources into BigQuery for comprehensive analytics. Other customers use our connectivity to analyze and report on their Google BigQuery data, with many customers using both solutions.
For more details on how CData enhances your Google BigQuery experience, check out our blog post: https://www.cdata.com/blog/what-is-bigquery
When the installation is complete, you are ready to configure your Code Assist MCP add-on by connecting to BigQuery.
NOTE: If the wizard does not open automatically, search for "CData Code Assist MCP for Google BigQuery" in the Windows search bar and open the application.
π Opening the CData Code Assist MCP add-on configuration wizard (Google Sheets is shown).Enter the appropriate connection properties in the configuration wizard
Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.
OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values.
In addition to the OAuth values, specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.
π Configuring the Code Assist MCP add-on connectionThis process creates a .mcp configuration file that Cursor will reference when launching the Code Assist MCP add-on. Now with your Code Assist MCP add-on configured, you are ready to connect it to Cursor.
{
"mcpServers": {
"cdata-local": {
"command": "C:/Program Files/Java/jdk-17/bin/java.exe",
"args": [
"-jar",
"C:/Program Files/CData/CData Code Assist MCP for Google BigQuery/lib/cdata.mcp.googlebigquery.jar",
"cdata_googlebigquery"
]
}
}
}
π Configuring the CData Code Assist MCP add-on in CursorNOTE: The command value should point to your Java 17+ java.exe executable, and the JAR path should point to the installed CData Code Assist MCP add-on .jar file. The final argument must match the MCP configuration name you saved in the CData configuration wizard (e.g. "cdata_googlebigquery").
"List all tables available in my BigQuery data connection."
π Querying live data from CursorCursor is now fully integrated with CData Code Assist MCP for Google BigQuery and can use the MCP tools exposed to explore schemas and execute live queries against BigQuery.
Download Code Assist MCP for free and give your AI tools schema-aware access to live BigQuery data during development. When you're ready to move to production, CData BigQuery Drivers deliver the same SQL-based access with enterprise-grade performance, security, and reliability.
Visit the CData Community to share insights, ask questions, and explore what's possible with MCP-powered AI workflows.
Download a free Google BigQuery Code Assist MCP to get started:
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π Google BigQuery IconThe CData Code Assist MCP for Google BigQuery provides schema-aware context for AI-assisted code generation with live Google BigQuery data.