> ## Documentation Index
> Fetch the complete documentation index at: https://veryfront.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Google BigQuery

> Explore BigQuery datasets and tables and run SQL queries against your Google Cloud data warehouse

## At a glance

* **Availability**: Experimental ([how to enable](/cloud/integrations)).
* **Auth**: OAuth 2.0.
* **Connection**: A user authorizes the connection in the provider's consent screen.
* **Scopes**: `https://www.googleapis.com/auth/cloud-platform.read-only`.
* **Optional scopes**: `https://www.googleapis.com/auth/bigquery`.
* **Docs**: [https://cloud.google.com/bigquery/docs/authentication](https://cloud.google.com/bigquery/docs/authentication)

## Credentials

Set these per environment. See [Connect an integration](/cloud/integrations).

With a managed OAuth app, Connect works without these variables; set them to use your own OAuth app instead.

| Variable               | Required | Description                                                                           |
| ---------------------- | -------- | ------------------------------------------------------------------------------------- |
| `GOOGLE_CLIENT_ID`     | Yes      | Google OAuth Client ID [Docs](https://console.cloud.google.com/apis/credentials).     |
| `GOOGLE_CLIENT_SECRET` | Yes      | Google OAuth Client Secret [Docs](https://console.cloud.google.com/apis/credentials). |

## Setup

1. **Create or reuse a Google Cloud project**: Go to [https://console.cloud.google.com](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](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](https://cloud.google.com/bigquery/docs/reference/rest)

## Tools

| Tool               | Access | Description                                                                                                                                                                                                                                                                           |
| ------------------ | ------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| List Projects      | Read   | List the Google Cloud projects the connected user can access, so agents can discover the projectId every other tool requires                                                                                                                                                          |
| List Datasets      | Read   | List all BigQuery datasets in a Google Cloud project                                                                                                                                                                                                                                  |
| List Tables        | Read   | List tables and views in a BigQuery dataset                                                                                                                                                                                                                                           |
| Get Table Schema   | Read   | Get a table's metadata including its full column schema, row count, and size                                                                                                                                                                                                          |
| Preview Table Data | Read   | Read sample rows directly from a table without running a query - no bytes are billed, ideal for inspecting data before writing SQL                                                                                                                                                    |
| Run SQL Query      | Read   | Run a read/SELECT GoogleSQL query and return the first page of results. With the default read-only scope only SELECT queries work; DML statements (INSERT/UPDATE/DELETE) require the full [https://www.googleapis.com/auth/bigquery](https://www.googleapis.com/auth/bigquery) scope. |
| Get Query Results  | Read   | Fetch additional pages of results from a previously started query job, using the jobId and pageToken returned by Run SQL Query                                                                                                                                                        |

## 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.