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At a glance

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Credentials

Set these per environment. See Connect an integration. With a managed OAuth app, Connect works without these variables; set them to use your own OAuth app instead.

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Setup

  1. Create or reuse a Google Cloud project: Go to https://console.cloud.google.com and create a project (or reuse the one that already hosts your Google OAuth app). Note its project ID - every BigQuery call takes a projectId parameter (the same value you would put in a GOOGLE_CLOUD_PROJECT env var). Without billing enabled you can still use the free BigQuery sandbox to test queries against public datasets.
  2. Enable the BigQuery API: Open https://console.cloud.google.com/apis/library/bigquery.googleapis.com, select your project, and click Enable.
  3. Configure the OAuth consent screen and client: Under APIs & Services > Credentials, create (or reuse) an OAuth 2.0 Client ID of type Web application. Add your app’s redirect URI ending in /api/auth/google-bigquery/callback. The same Google OAuth client can be shared across all Google connectors.
  4. Set environment variables: Set GOOGLE_CLIENT_ID and GOOGLE_CLIENT_SECRET from the OAuth client you created. These are shared with other Google integrations.
  • The default scope is read-only (cloud-platform.read-only): listing datasets/tables and SELECT queries work, but DML (INSERT/UPDATE/DELETE) requires granting the optional full bigquery scope.
  • There is no project-wide env var: pass the Google Cloud project ID as the projectId parameter on every call (treat it like GOOGLE_CLOUD_PROJECT).
  • Queries are billed to the projectId you pass; use dryRun to estimate bytes processed before running expensive queries.
  • The query API defaults to legacy SQL - keep useLegacySql set to false to write GoogleSQL.
Provider API reference: https://cloud.google.com/bigquery/docs/reference/rest

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Tools

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Example prompts

  • List the datasets and tables in my BigQuery project and summarize what data is available.
  • Write and run a BigQuery SQL query to answer a question about my data, then explain the results.
  • Show me the schema of a BigQuery table and suggest useful queries against it.