> **Building with AI coding agents?** If you're using an AI coding agent, install the official Scalekit plugin. It gives your agent full awareness of the Scalekit API — reducing hallucinations and enabling faster, more accurate code generation.
>
> - **Claude Code**: `/plugin marketplace add scalekit-inc/claude-code-authstack` then `/plugin install <auth-type>@scalekit-auth-stack`
> - **GitHub Copilot CLI**: `copilot plugin marketplace add scalekit-inc/github-copilot-authstack` then `copilot plugin install <auth-type>@scalekit-auth-stack`
> - **Codex**: run the bash installer, restart, then open Plugin Directory and enable `<auth-type>`
> - **Skills CLI** (Windsurf, Cline, 40+ agents): `npx skills add scalekit-inc/skills --list` then `--skill <skill-name>`
>
> `<auth-type>` / `<skill-name>`: `agentkit`, `full-stack-auth`, `mcp-auth`, `modular-sso`, `modular-scim` — [Full setup guide](https://docs.scalekit.com/dev-kit/build-with-ai/)

---

# BigQuery (Service Account)
<div class="grid grid-cols-5 gap-4 items-center">
 <div class="col-span-4">
  Connect to BigQuery using a GCP service account for server-to-server authentication without user login.
 </div>
 <div class="flex justify-center">
  <img src="https://cdn.scalekit.com/sk-connect/assets/provider-icons/bigquery.svg" width="64" height="64" alt="BigQuery (Service Account) logo" />
 </div>
</div>

Supports authentication: 

## Create a Connection

In [Scalekit dashboard](https://app.scalekit.com), go to **AgentKit** > **Connections** > **Create Connection**. Find **BigQuery (Service Account)** and click **Create**.

That's it — no OAuth credentials or redirect URIs needed. BigQuery Service Account uses server-to-server authentication handled entirely through your GCP service account credentials.

## Create a Connected Account

To connect a BigQuery account programmatically, you need a GCP service account JSON key. Here's how to get one:

1. ### Create a GCP service account

    - Go to [Google Cloud Console](https://console.cloud.google.com) → **IAM & Admin** → **Service Accounts**.
    - Click **+ Create Service Account**, enter a name and description, and click **Create and Continue**.
    - Grant the service account the **BigQuery Data Viewer**, **BigQuery Data Editor**, and **BigQuery Job User** roles, then click **Done**.

2. ### Enable the BigQuery API

    - In [Google Cloud Console](https://console.cloud.google.com), go to **APIs & Services** → **Library**.
    - Search for **BigQuery API** and click **Enable**.

      > Image: Enable BigQuery API in Google Cloud Console

3. ### Download the service account JSON key

    - In the Service Accounts list, click on your service account.
    - Go to the **Keys** tab → **Add Key** → **Create new key**.
    - Select **JSON** and click **Create**. The key file downloads automatically.
    - Use the contents of this file as the `service_account_json` value when creating a connected account.

## Usage

export const sectionTitle = 'Common workflows'

## Execute a tool

  ### Node.js

```typescript
const result = await actions.executeTool({
  connector: 'bigqueryserviceaccount',
  identifier: 'user_123',
  toolName: 'bigqueryserviceaccount_run_query',
  toolInput: {
    query: 'SELECT 1 AS test',
  },
});
console.log(result);
```

  ### Python

```python
result = actions.execute_tool(
    connection_name='bigqueryserviceaccount',
    identifier='user_123',
    tool_name='bigqueryserviceaccount_run_query',
    tool_input={
        "query": "SELECT 1 AS test",
    },
)
print("Query result:", result.data)
```

## Proxy API call

> tip: Project ID is resolved automatically
>
> Scalekit automatically resolves the GCP project ID in the base URL from the connected service account credentials. You only need to provide the path relative to the project, e.g. `/datasets` or `/datasets/{datasetId}/tables`.

  ### Node.js

```typescript
const result = await actions.request({
  connectionName: 'bigqueryserviceaccount',
  identifier: 'user_123',
  path: '/datasets',
  method: 'GET',
});
console.log(result);
```

  ### Python

```python
result = actions.request(
    connection_name='bigqueryserviceaccount',
    identifier='user_123',
    path="/datasets",
    method="GET",
)
print(result)
```

## Tool list

## `bigqueryserviceaccount_get_dataset`

Retrieve metadata for a specific BigQuery dataset, including location, description, labels, access controls, and creation/modification times.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `dataset_id` | string | Yes | The ID of the dataset to retrieve |

## `bigqueryserviceaccount_get_job`

Retrieve the status and configuration of a BigQuery job by its job ID. Use this to poll for completion of an async query job.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `job_id` | string | Yes | The ID of the job to retrieve |
| `location` | string | No | Geographic location where the job was created, e.g. US or EU |

## `bigqueryserviceaccount_get_model`

Retrieve metadata for a specific BigQuery ML model, including model type, feature columns, label columns, and training run details.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `dataset_id` | string | Yes | The ID of the dataset containing the model |
| `model_id` | string | Yes | The ID of the model to retrieve |

## `bigqueryserviceaccount_get_query_results`

Retrieve the results of a completed BigQuery query job. Supports pagination via page tokens. Use after polling Get Job until status is DONE.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `job_id` | string | Yes | The ID of the completed query job |
| `location` | string | No | Geographic location where the job was created, e.g. US or EU |
| `max_results` | integer | No | Maximum number of rows to return per page |
| `page_token` | string | No | Page token from a previous response to retrieve the next page of results |
| `timeout_ms` | integer | No | Maximum milliseconds to wait if the query has not yet completed |

## `bigqueryserviceaccount_get_routine`

Retrieve the definition and metadata of a specific BigQuery routine (stored procedure or UDF), including its arguments, return type, and body.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `dataset_id` | string | Yes | The ID of the dataset containing the routine |
| `routine_id` | string | Yes | The ID of the routine to retrieve |

## `bigqueryserviceaccount_get_table`

Retrieve metadata and schema for a specific BigQuery table or view, including column names, types, descriptions, and table properties.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `dataset_id` | string | Yes | The ID of the dataset containing the table |
| `table_id` | string | Yes | The ID of the table or view to retrieve |

## `bigqueryserviceaccount_list_datasets`

List all BigQuery datasets in the project. Supports filtering by label and pagination.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `all` | boolean | No | If true, includes hidden datasets in the results |
| `filter` | string | No | Label filter expression to restrict results, e.g. labels.env:prod |
| `max_results` | integer | No | Maximum number of datasets to return per page |
| `page_token` | string | No | Page token from a previous response to retrieve the next page |

## `bigqueryserviceaccount_list_jobs`

List BigQuery jobs in the project. Supports filtering by state and projection, and pagination.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `all_users` | boolean | No | If true, returns jobs for all users in the project; otherwise returns only the current user's jobs |
| `max_results` | integer | No | Maximum number of jobs to return per page |
| `page_token` | string | No | Page token from a previous response to retrieve the next page |
| `projection` | string | No | Controls the fields returned: minimal (default) or full |
| `state_filter` | string | No | Filter jobs by state: done, pending, or running |

## `bigqueryserviceaccount_list_models`

List all BigQuery ML models in a dataset, including their model type, training status, and creation time.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `dataset_id` | string | Yes | The ID of the dataset to list models from |
| `max_results` | integer | No | Maximum number of models to return per page |
| `page_token` | string | No | Page token from a previous response to retrieve the next page |

## `bigqueryserviceaccount_list_routines`

List all stored procedures and user-defined functions (UDFs) in a BigQuery dataset.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `dataset_id` | string | Yes | The ID of the dataset to list routines from |
| `filter` | string | No | Filter expression to restrict results, e.g. routineType:SCALAR_FUNCTION |
| `max_results` | integer | No | Maximum number of routines to return per page |
| `page_token` | string | No | Page token from a previous response to retrieve the next page |

## `bigqueryserviceaccount_list_table_data`

Read rows directly from a BigQuery table without writing a SQL query. Supports pagination, row offset, and field selection.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `dataset_id` | string | Yes | The ID of the dataset containing the table |
| `max_results` | integer | No | Maximum number of rows to return per page |
| `page_token` | string | No | Page token from a previous response to retrieve the next page |
| `selected_fields` | string | No | Comma-separated list of fields to return; if omitted all fields are returned |
| `start_index` | integer | No | Zero-based row index to start reading from |
| `table_id` | string | Yes | The ID of the table to read rows from |

## `bigqueryserviceaccount_list_tables`

List all tables and views in a BigQuery dataset. Supports pagination.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `dataset_id` | string | Yes | The ID of the dataset to list tables from |
| `max_results` | integer | No | Maximum number of tables to return per page |
| `page_token` | string | No | Page token from a previous response to retrieve the next page |

## `bigqueryserviceaccount_run_query`

Execute a SQL query synchronously against BigQuery and return results immediately. Best for short-running queries.

| Name | Type | Required | Description |
| --- | --- | --- | --- |
| `create_session` | boolean | No | If true, creates a new session and returns a session ID in the response |
| `dry_run` | boolean | No | If true, validates the query and returns estimated bytes processed without executing |
| `location` | string | No | Geographic location of the dataset, e.g. US or EU |
| `max_results` | integer | No | Maximum number of rows to return in the response |
| `query` | string | Yes | SQL query to execute |
| `timeout_ms` | integer | No | Maximum milliseconds to wait for query completion before returning |
| `use_legacy_sql` | boolean | No | Use BigQuery legacy SQL syntax instead of standard SQL |


---

## More Scalekit documentation

| Resource | What it contains | When to use it |
|----------|-----------------|----------------|
| [/llms.txt](/llms.txt) | Structured index with routing hints per product area | Start here — find which documentation set covers your topic before loading full content |
| [/llms-full.txt](/llms-full.txt) | Complete documentation for all Scalekit products in one file | Use when you need exhaustive context across multiple products or when the topic spans several areas |
| [sitemap-0.xml](https://docs.scalekit.com/sitemap-0.xml) | Full URL list of every documentation page | Use to discover specific page URLs you can fetch for targeted, page-level answers |