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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
SELECTstatements.INSERT,UPDATE, andDELETEstatements and stored procedures aren't allowed. If the query doesn't include aSELECTstatement, an error is returned. For information on creating queries, see the GoogleSQL documentation. Theexecute_sqltool can also have side effects if the query invokes remote functions or Python UDFs. All queries that are run using theexecute_sqltool have a label that identifies the tool as the source. You can use this label to filter the queries using the label and value pairgoog-mcp-server: true. Queries are charged to the project specified in theproject_idfield.
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.