/bigquery-basics

BigQuery MCP Usage

BigQuery is supported by a remote Model Context Protocol (MCP) server that provides a set of tools for automated data management and analysis.

MCP Tools for BigQuery

• list_dataset_ids: List BigQuery dataset IDs in a Google Cloud project.
• get_dataset_info: Get metadata information about a BigQuery dataset.
• list_table_ids: List table ids in a BigQuery dataset.
• get_table_info: Get metadata information about a BigQuery table.
• execute_sql: Run a SQL query in the project and return the result. This tool is restricted to only SELECT statements. INSERT, UPDATE, and DELETE statements and stored procedures aren't allowed. If the query doesn't include a SELECT statement, an error is returned. For information on creating queries, see the GoogleSQL documentation. The execute_sql tool can also have side effects if the query invokes remote functions or Python UDFs. All queries that are run using the execute_sql tool have a label that identifies the tool as the source. You can use this label to filter the queries using the label and value pair goog-mcp-server: true. Queries are charged to the project specified in the project_id field.

Setup Instructions

To connect to the BigQuery MCP server, see Configure a client connection.

Supported Operations

Agents using the BigQuery MCP remote server can perform tasks such as:

• Answering questions about data by generating and running SQL.
• Getting dataset metadata.
• Getting table metadata.

For more information about the BigQuery MCP server, visit: Use the BigQuery MCP server. Alternatively, you can use MCP Toolbox, an open-source CLI tool that runs a local MCP server for BigQuery connections. For more on connecting BigQuery to your tools, see Connect LLMs to BigQuery with MCP for details. For additional specialized skills and advanced analytics workflows, install the BigQuery Data Analytics extension for the Gemini CLI or plugin for Claude Code and Codex.