Google BigQuery
Run SQL queries, explore datasets, and analyze data in BigQuery using natural language
Tools
9 available tools
Search the tool definitions captured from the MCP server. The live list can vary with version, deployment, and credentials.
9 tools found.
Cancel job
Tool name:
cancel_jobCancel a running BigQuery job.
Execute SQL
Tool name:
execute_sqlRun a SQL query in the project and return the result.
Execute SQL readonly
Tool name:
execute_sql_readonlyRun a read-only SQL query in the project and return the result.
Get dataset info
Tool name:
get_dataset_infoGet metadata information about a BigQuery dataset or BigLake namespace.
Get job
Tool name:
get_jobGet information and status about a BigQuery job.
Get query results
Tool name:
get_query_resultsGet the results of a BigQuery SQL query job.
Get table info
Tool name:
get_table_infoGet metadata information about a BigQuery table or BigLake table.
List dataset IDS
Tool name:
list_dataset_idsList BigQuery dataset IDs and BigLake namespaces in a Google Cloud project.
List table IDS
Tool name:
list_table_idsList table ids in a BigQuery dataset or BigLake namespace.
Compatibility
Connect Google BigQuery to the clients your teams already use
27 AI clients can connect to Google BigQuery through Runlayer. Pick one for its setup steps and controls.
Why Runlayer
Why connect Google BigQuery through Runlayer?
One approved path
Approve Google BigQuery once and everyone gets it in the AI clients they already use. Runlayer holds the credentials, so nothing sits in client configuration.
Fine-grained policy
Control who can use each server, tool, and resource, and under which runtime conditions.
Runtime security
Scan tool inputs and outputs for prompt injection, credential exposure, PII, and data exfiltration, then alert, mask, or block.
Audit and observability
Trace every request across users, clients, tools, policies, scanner decisions, and downstream systems.