Google BigQuery connector

1. Install the SDK

   Node.js

   npm install @scalekit-sdk/node

   Python

   pip install scalekit

   Full SDK reference: Node.js | Python

2. Set your credentials

   Add your Scalekit credentials to your .env file. Find values in app.scalekit.com > Developers > API Credentials.

   .env

   SCALEKIT_ENVIRONMENT_URL=<your-environment-url>
   SCALEKIT_CLIENT_ID=<your-client-id>
   SCALEKIT_CLIENT_SECRET=<your-client-secret>

3. Set up the connector

   Register your Google BigQuery credentials with Scalekit so it handles the token lifecycle. You do this once per environment.

   Dashboard setup steps

   Register your Scalekit environment with the Google BigQuery connector so Scalekit handles the authentication flow and token lifecycle for you. The connection name you create will be used to identify and invoke the connection programmatically. Then complete the configuration in your application as follows:

   1. Set up auth redirects

      • In Scalekit dashboard, go to AgentKit > Connections > Create Connection. Find Google BigQuery and click Create. Click Use your own credentials and copy the redirect URI. It looks like https://<SCALEKIT_ENVIRONMENT_URL>/sso/v1/oauth/<CONNECTION_ID>/callback.

        

      • Navigate to Google Cloud Console → APIs & Services → Credentials. Select + Create Credentials, then OAuth client ID. Choose Web application from the Application type menu.

        

      • Under Authorized redirect URIs, click + Add URI, paste the redirect URI, and click Create.

        

   2. Enable the BigQuery API

      • In Google Cloud Console, go to APIs & Services → Library. Search for “BigQuery API” and click Enable.

        

   3. Get client credentials

      • Google provides your Client ID and Client Secret after you create the OAuth client ID in step 1.

   4. Add credentials in Scalekit

      • In Scalekit dashboard, go to AgentKit > Connections and open the connection you created.

      • Enter your credentials:

        • Client ID (from above)
        • Client Secret (from above)
        • Permissions (scopes — see Google API Scopes reference)

        

      • Click Save.

4. Authorize and make your first call

   Node.js

   quickstart.ts

   import { ScalekitClient } from '@scalekit-sdk/node'
   import 'dotenv/config'
   
   const scalekit = new ScalekitClient(
     process.env.SCALEKIT_ENV_URL,
     process.env.SCALEKIT_CLIENT_ID,
     process.env.SCALEKIT_CLIENT_SECRET,
   )
   const actions = scalekit.actions
   
   const connector = 'bigquery'
   const identifier = 'user_123'
   
   // Generate an authorization link for the user
   const { link } = await actions.getAuthorizationLink({ connectionName: connector, identifier })
   console.log('Authorize Google BigQuery:', link)
   process.stdout.write('Press Enter after authorizing...')
   await new Promise(r => process.stdin.once('data', r))
   
   // Make your first call
   const result = await actions.executeTool({
     connector,
     identifier,
     toolName: 'bigquery_list_projects',
     toolInput: {},
   })
   console.log(result)

   Python

   quickstart.py

   import os
   from scalekit.client import ScalekitClient
   from dotenv import load_dotenv
   load_dotenv()
   
   scalekit_client = ScalekitClient(
       env_url=os.getenv("SCALEKIT_ENV_URL"),
       client_id=os.getenv("SCALEKIT_CLIENT_ID"),
       client_secret=os.getenv("SCALEKIT_CLIENT_SECRET"),
   )
   actions = scalekit_client.actions
   
   connection_name = "bigquery"
   identifier = "user_123"
   
   # Generate an authorization link for the user
   link_response = actions.get_authorization_link(
       connection_name=connection_name,
       identifier=identifier,
   )
   print("Authorize Google BigQuery:", link_response.link)
   input("Press Enter after authorizing...")
   
   # Make your first call
   result = actions.execute_tool(
       tool_input={},
       tool_name="bigquery_list_projects",
       connection_name=connection_name,
       identifier=identifier,
   )
   print(result)

What you can do

Connect this agent connector to let your agent:

• Update table, row access policy, routine — Update metadata for an existing BigQuery table, such as its schema (e.g
• Dataset undelete, replace, insert — Restore a recently deleted BigQuery dataset
• Permissions test table iam, test row access policy iam, test routine iam — Check which of a given set of IAM permissions the caller has on a BigQuery table or view
• Policy set table iam, set routine iam, insert row access — Set the IAM access control policy on a BigQuery table or view, replacing any existing policy bindings
• Run query — Execute a SQL query synchronously against BigQuery and return results immediately
• Table replace, insert — Full replace of a table’s mutable metadata (PUT semantics) — any field you omit will be reset to its default, unlike bigquery_update_table which only changes fields you provide

Common workflows

Proxy API call

Node.js

const result = await actions.request({
  connectionName: 'bigquery',
  identifier: 'user_123',
  path: '/bigquery/v2/projects',
  method: 'GET',
});
console.log(result);

Python

result = actions.request(
    connection_name='bigquery',
    identifier='user_123',
    path="/bigquery/v2/projects",
    method="GET",
)
print(result)

Execute a tool

Node.js

const result = await actions.executeTool({
  connector: 'bigquery',
  identifier: 'user_123',
  toolName: 'bigquery_list',
  toolInput: {},
});
console.log(result);

Python

result = actions.execute_tool(
    connection_name='bigquery',
    identifier='user_123',
    tool_name='bigquery_list',
    tool_input={},
)
print(result)

Google OAuth consent screen verification

Before you use your own Google OAuth credentials in production, understand what end users see on Google’s consent screen when they authorize a connected account.

Audience type	Consent screen behavior	When to use
Internal	Shows your App Name and logo from Branding settings	Only users in your Google Workspace or Cloud Identity organization can authorize the connector
External	Shows {env_name}.scalekit.dev until Google verifies your app	Any user with a Google account can authorize the connector

Why External is required for most AgentKit connectors:

• Internal restricts authorization to users in your Google Workspace or Cloud Identity organization. Users with @gmail.com or other Google accounts outside your organization cannot complete OAuth.
• External is required when end users outside your organization authorize tool access through connected accounts.
• Organization-managed OAuth clients follow the same rules as personal or developer OAuth clients. Switching to an org-owned client does not bypass Google verification.
• Until Google completes verification of your External app, users see scalekit.dev on the consent screen. After verification, your App Name and logo appear.

Separate from Scalekit domain verification

This is Google’s OAuth consent screen behavior—not Scalekit’s. Custom domain controls your Scalekit environment URL for MCP auth branding. It does not replace Google’s app verification for OAuth consent screens.

During development:

• Add Test users under APIs & Services → OAuth consent screen while publishing status is Testing.
• On unverified apps, users can click Advanced → Go to app (unsafe) to proceed during testing.
• Google Workspace admins may need to allowlist your OAuth client.

For Google’s verification requirements and timeline, refer to Google’s OAuth consent screen verification guide.

Tool list

Use the exact tool names from the Tool list below when you call execute_tool. If you’re not sure which name to use, list the tools available for the current user first.

bigquery_batch_delete_row_access_policies#Delete multiple row access policies from a BigQuery table in a single call.5 params▾

Delete multiple row access policies from a BigQuery table in a single call.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

policy_idsarrayrequiredThe IDs of the row access policies to delete

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table to delete row access policies from

forcebooleanoptionalIf true, allows removing all row access policies on the table even though this would make the table fully accessible to all existing table readers

bigquery_cancel_job#Request cancellation of a running BigQuery job. Cancellation is best-effort; the job may complete before the cancellation takes effect.3 params▾

Request cancellation of a running BigQuery job. Cancellation is best-effort; the job may complete before the cancellation takes effect.

NameTypeRequiredDescription

job_idstringrequiredThe ID of the job to cancel

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

locationstringoptionalGeographic location where the job was created, e.g. US or EU

bigquery_delete_dataset#Delete a BigQuery dataset. By default the dataset must be empty; set delete_contents to true to also delete all tables within it.3 params▾

Delete a BigQuery dataset. By default the dataset must be empty; set delete_contents to true to also delete all tables within it.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset to delete

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

delete_contentsbooleanoptionalIf true, deletes all tables in the dataset before deleting the dataset itself

bigquery_delete_job#Delete a BigQuery job's metadata. This only works on jobs that are in a DONE state and still within the job retention window.3 params▾

Delete a BigQuery job's metadata. This only works on jobs that are in a DONE state and still within the job retention window.

NameTypeRequiredDescription

job_idstringrequiredThe ID of the DONE job to delete

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

locationstringoptionalGeographic location where the job was created, e.g. US or EU

bigquery_delete_model#Delete a BigQuery ML model from a dataset. This permanently removes the model and cannot be undone.3 params▾

Delete a BigQuery ML model from a dataset. This permanently removes the model and cannot be undone.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the model

model_idstringrequiredThe ID of the model to delete

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

bigquery_delete_routine#Delete a stored procedure or user-defined function (UDF) from a BigQuery dataset. This permanently removes the routine and cannot be undone.3 params▾

Delete a stored procedure or user-defined function (UDF) from a BigQuery dataset. This permanently removes the routine and cannot be undone.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the routine

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

routine_idstringrequiredThe ID of the routine to delete

bigquery_delete_row_access_policy#Permanently delete a row access policy from a BigQuery table.5 params▾

Permanently delete a row access policy from a BigQuery table.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

row_access_policy_idstringrequiredThe ID of the row access policy to delete

table_idstringrequiredThe ID of the table containing the row access policy

forcebooleanoptionalIf true, allows deleting the last remaining row access policy on the table even though this would make the table fully accessible to all existing table readers

bigquery_delete_table#Permanently delete a BigQuery table or view from a dataset.3 params▾

Permanently delete a BigQuery table or view from a dataset.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table to delete

bigquery_get_dataset#Retrieve metadata for a specific BigQuery dataset, including location, description, labels, access controls, and creation/modification times.2 params▾

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

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset to retrieve

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

bigquery_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 submitted via Insert Query Job.3 params▾

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

NameTypeRequiredDescription

job_idstringrequiredThe ID of the job to retrieve

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

locationstringoptionalGeographic location where the job was created, e.g. US or EU

bigquery_get_model#Retrieve metadata for a specific BigQuery ML model, including model type, feature columns, label columns, and training run details.3 params▾

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

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the model

model_idstringrequiredThe ID of the model to retrieve

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

bigquery_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.6 params▾

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

NameTypeRequiredDescription

job_idstringrequiredThe ID of the completed query job

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

locationstringoptionalGeographic location where the job was created, e.g. US or EU

max_resultsintegeroptionalMaximum number of rows to return per page

page_tokenstringoptionalPage token from a previous response to retrieve the next page of results

timeout_msintegeroptionalMaximum milliseconds to wait if the query has not yet completed

bigquery_get_routine#Retrieve the definition and metadata of a specific BigQuery routine (stored procedure or UDF), including its arguments, return type, and body.3 params▾

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

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the routine

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

routine_idstringrequiredThe ID of the routine to retrieve

bigquery_get_routine_iam_policy#Retrieve the IAM access control policy currently set on a BigQuery routine (stored procedure or UDF).4 params▾

Retrieve the IAM access control policy currently set on a BigQuery routine (stored procedure or UDF).

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the routine

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

routine_idstringrequiredThe ID of the routine to fetch the IAM policy for

requested_policy_versionintegeroptionalThe policy format version to be returned

bigquery_get_row_access_policy#Retrieve the definition of a single row access policy on a BigQuery table.4 params▾

Retrieve the definition of a single row access policy on a BigQuery table.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

row_access_policy_idstringrequiredThe ID of the row access policy to retrieve

table_idstringrequiredThe ID of the table containing the row access policy

bigquery_get_row_access_policy_iam_policy#Retrieve the IAM policy for a row access policy on a BigQuery table.5 params▾

Retrieve the IAM policy for a row access policy on a BigQuery table.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

row_access_policy_idstringrequiredThe ID of the row access policy to get the IAM policy for

table_idstringrequiredThe ID of the table containing the row access policy

requested_policy_versionintegeroptionalThe IAM policy format version to be returned

bigquery_get_service_account#Retrieve the email address of the BigQuery-managed service account for this project. Used, for example, to grant that service account access to a Cloud Storage bucket for load or export jobs.1 param▾

Retrieve the email address of the BigQuery-managed service account for this project. Used, for example, to grant that service account access to a Cloud Storage bucket for load or export jobs.

NameTypeRequiredDescription

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

bigquery_get_table#Retrieve metadata and schema for a specific BigQuery table or view, including column names, types, descriptions, and table properties.3 params▾

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

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table or view to retrieve

bigquery_get_table_iam_policy#Retrieve the IAM access control policy currently set on a BigQuery table or view.4 params▾

Retrieve the IAM access control policy currently set on a BigQuery table or view.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table to fetch the IAM policy for

requested_policy_versionintegeroptionalThe policy format version to be returned

bigquery_insert_dataset#Create a new BigQuery dataset in the specified project.8 params▾

Create a new BigQuery dataset in the specified project.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID to assign to the new dataset

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

default_partition_expiration_msintegeroptionalDefault lifetime in milliseconds for partitions in partitioned tables created in this dataset

default_table_expiration_msintegeroptionalDefault lifetime in milliseconds for tables created in this dataset

descriptionstringoptionalA description of the dataset

friendly_namestringoptionalA human-readable display name for the dataset

labelsobjectoptionalKey-value labels to attach to the dataset, e.g. {"env": "prod"}

locationstringoptionalGeographic location where the dataset should be created, e.g. US or EU

bigquery_insert_job#Submit an asynchronous BigQuery job (load, extract, copy, or query). Use this instead of Run Query for long-running or non-query operations. Poll the job status with Get Job, then fetch results with Get Query Results if it was a query job.5 params▾

Submit an asynchronous BigQuery job (load, extract, copy, or query). Use this instead of Run Query for long-running or non-query operations. Poll the job status with Get Job, then fetch results with Get Query Results if it was a query job.

NameTypeRequiredDescription

configurationobjectrequiredThe full BigQuery JobConfiguration resource. Must specify exactly one of query, load, extract, or copy. See https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobConfiguration for the full schema.

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

job_idstringoptionalCaller-specified job ID, used later to poll the job with Get Job

labelsobjectoptionalLabels to attach to the job as key-value pairs

locationstringoptionalGeographic location where the job should run, e.g. US or EU

bigquery_insert_routine#Create a new stored procedure or user-defined function (UDF) in a BigQuery dataset.9 params▾

Create a new stored procedure or user-defined function (UDF) in a BigQuery dataset.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset to create the routine in

definition_bodystringrequiredThe SQL body of the function or procedure

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

routine_idstringrequiredThe ID to assign to the new routine

routine_typestringrequiredThe type of routine to create

argumentsarrayoptionalThe list of input/output arguments for the routine

descriptionstringoptionalA description of the routine

languagestringoptionalThe language of the routine body

return_typeobjectoptionalThe return type of the routine, as a StandardSqlDataType object

bigquery_insert_row_access_policy#Create a new row access policy on a BigQuery table, restricting which rows a set of grantee principals can see via a SQL boolean filter predicate.6 params▾

Create a new row access policy on a BigQuery table, restricting which rows a set of grantee principals can see via a SQL boolean filter predicate.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

filter_predicatestringrequiredA SQL boolean expression restricting which rows are visible to the grantees

granteesarrayrequiredThe principals this row access policy grants row visibility to

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

row_access_policy_idstringrequiredThe ID to assign to the new row access policy

table_idstringrequiredThe ID of the table to create the row access policy on

bigquery_insert_table#Create a new BigQuery table or view in the specified dataset.8 params▾

Create a new BigQuery table or view in the specified dataset.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset in which to create the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID to assign to the new table

descriptionstringoptionalA description of the table

expiration_timestringoptionalThe time when this table expires, in milliseconds since the epoch, as a string

friendly_namestringoptionalA human-readable display name for the table

labelsobjectoptionalKey-value labels to attach to the table, e.g. {"env": "prod"}

schemaobjectoptionalThe table schema, as the BigQuery Table.schema resource: an object with a 'fields' array, e.g. {"fields":[{"name":"col1","type":"STRING"}]}

bigquery_insert_table_data#Stream insert rows directly into a BigQuery table via the tabledata.insertAll API.7 params▾

Stream insert rows directly into a BigQuery table via the tabledata.insertAll API.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

rowsarrayrequiredRows to insert, where each row is a plain object mapping column names to values

table_idstringrequiredThe ID of the table to insert rows into

ignore_unknown_valuesbooleanoptionalIf true, values for fields not present in the table schema are ignored

skip_invalid_rowsbooleanoptionalIf true, rows with invalid data are skipped and remaining valid rows are inserted

template_suffixstringoptionalIf specified, rows are inserted into a template table named tableId + templateSuffix

bigquery_list_datasets#List all BigQuery datasets in the project. Supports filtering by label and pagination.5 params▾

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

NameTypeRequiredDescription

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

allbooleanoptionalIf true, includes hidden datasets in the results

filterstringoptionalLabel filter expression to restrict results, e.g. labels.env:prod

max_resultsintegeroptionalMaximum number of datasets to return per page

page_tokenstringoptionalPage token from a previous response to retrieve the next page

bigquery_list_jobs#List BigQuery jobs in the project. Supports filtering by state and projection, and pagination.6 params▾

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

NameTypeRequiredDescription

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

all_usersbooleanoptionalIf true, returns jobs for all users in the project; otherwise returns only the current user's jobs

max_resultsintegeroptionalMaximum number of jobs to return per page

page_tokenstringoptionalPage token from a previous response to retrieve the next page

projectionstringoptionalControls the fields returned: minimal (default) or full

state_filterstringoptionalFilter jobs by state: done, pending, or running

bigquery_list_models#List all BigQuery ML models in a dataset, including their model type, training status, and creation time.4 params▾

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

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset to list models from

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

max_resultsintegeroptionalMaximum number of models to return per page

page_tokenstringoptionalPage token from a previous response to retrieve the next page

bigquery_list_projects#List Google Cloud projects accessible to the authenticated account that have BigQuery enabled. Use this first to discover valid project_id values for every other bigquery_* tool.2 params▾

List Google Cloud projects accessible to the authenticated account that have BigQuery enabled. Use this first to discover valid project_id values for every other bigquery_* tool.

NameTypeRequiredDescription

max_resultsintegeroptionalMaximum number of projects to return per page

page_tokenstringoptionalPage token from a previous response to retrieve the next page

bigquery_list_routines#List all stored procedures and user-defined functions (UDFs) in a BigQuery dataset.5 params▾

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

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset to list routines from

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

filterstringoptionalFilter expression to restrict results, e.g. routineType:SCALAR_FUNCTION

max_resultsintegeroptionalMaximum number of routines to return per page

page_tokenstringoptionalPage token from a previous response to retrieve the next page

bigquery_list_row_access_policies#List the row access policies defined on a BigQuery table. Supports pagination.4 params▾

List the row access policies defined on a BigQuery table. Supports pagination.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table to list row access policies for

page_tokenstringoptionalPage token from a previous response to retrieve the next page

bigquery_list_table_data#Read rows directly from a BigQuery table without writing a SQL query. Supports pagination, row offset, and field selection.7 params▾

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

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table to read rows from

max_resultsintegeroptionalMaximum number of rows to return per page

page_tokenstringoptionalPage token from a previous response to retrieve the next page

selected_fieldsstringoptionalComma-separated list of fields to return; if omitted all fields are returned

start_indexintegeroptionalZero-based row index to start reading from

bigquery_list_tables#List all tables and views in a BigQuery dataset. Supports pagination.4 params▾

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

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset to list tables from

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

max_resultsintegeroptionalMaximum number of tables to return per page

page_tokenstringoptionalPage token from a previous response to retrieve the next page

bigquery_replace_dataset#Full replace of a dataset's mutable metadata (PUT semantics) — any field you omit will be reset to its default, unlike bigquery_update_dataset which only changes fields you provide.7 params▾

Full replace of a dataset's mutable metadata (PUT semantics) — any field you omit will be reset to its default, unlike bigquery_update_dataset which only changes fields you provide.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset to replace

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

default_partition_expiration_msintegeroptionalDefault lifetime in milliseconds for partitions in partitioned tables created in this dataset

default_table_expiration_msintegeroptionalDefault lifetime in milliseconds for tables created in this dataset

descriptionstringoptionalA description of the dataset

friendly_namestringoptionalA human-readable display name for the dataset

labelsobjectoptionalKey-value labels to attach to the dataset, e.g. {"env": "prod"}

bigquery_replace_table#Full replace of a table's mutable metadata (PUT semantics) — any field you omit will be reset to its default, unlike bigquery_update_table which only changes fields you provide.8 params▾

Full replace of a table's mutable metadata (PUT semantics) — any field you omit will be reset to its default, unlike bigquery_update_table which only changes fields you provide.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table to replace

descriptionstringoptionalA description of the table

expiration_timestringoptionalThe time when this table expires, in milliseconds since the epoch, as a string

friendly_namestringoptionalA human-readable display name for the table

labelsobjectoptionalKey-value labels to attach to the table, e.g. {"env": "prod"}

schemaobjectoptionalThe table schema, as the BigQuery Table.schema resource: an object with a 'fields' array, e.g. {"fields":[{"name":"col1","type":"STRING"}]}

bigquery_run_query#Execute a SQL query synchronously against BigQuery and return results immediately. Best for short-running queries. For long-running queries use Insert Query Job instead.8 params▾

Execute a SQL query synchronously against BigQuery and return results immediately. Best for short-running queries. For long-running queries use Insert Query Job instead.

NameTypeRequiredDescription

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

querystringrequiredSQL query to execute

create_sessionbooleanoptionalIf true, creates a new session and returns a session ID in the response

dry_runbooleanoptionalIf true, validates the query and returns estimated bytes processed without executing

locationstringoptionalGeographic location of the dataset, e.g. US or EU

max_resultsintegeroptionalMaximum number of rows to return in the response

timeout_msintegeroptionalMaximum milliseconds to wait for query completion before returning

use_legacy_sqlbooleanoptionalUse BigQuery legacy SQL syntax instead of standard SQL

bigquery_set_routine_iam_policy#Set the IAM access control policy on a BigQuery routine (stored procedure or UDF), replacing any existing policy bindings.6 params▾

Set the IAM access control policy on a BigQuery routine (stored procedure or UDF), replacing any existing policy bindings.

NameTypeRequiredDescription

bindingsarrayrequiredThe complete list of IAM policy bindings to set on the routine. Each item binds a role to a list of members, e.g. {"role":"roles/bigquery.dataViewer","members":["user:x@example.com"]}. This replaces the entire bindings list.

dataset_idstringrequiredThe ID of the dataset containing the routine

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

routine_idstringrequiredThe ID of the routine to set the IAM policy on

policy_etagstringoptionalThe etag of the existing policy, used for optimistic concurrency control

update_maskstringoptionalA FieldMask specifying which fields of the policy to modify

bigquery_set_table_iam_policy#Set the IAM access control policy on a BigQuery table or view, replacing any existing policy bindings.6 params▾

Set the IAM access control policy on a BigQuery table or view, replacing any existing policy bindings.

NameTypeRequiredDescription

bindingsarrayrequiredThe complete list of IAM policy bindings to set on the table. Each item binds a role to a list of members, e.g. {"role":"roles/bigquery.dataViewer","members":["user:x@example.com"]}. This replaces the entire bindings list.

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table to set the IAM policy on

policy_etagstringoptionalThe etag of the existing policy, used for optimistic concurrency control

update_maskstringoptionalA FieldMask specifying which fields of the policy to modify

bigquery_test_routine_iam_permissions#Check which of a given set of IAM permissions the caller has on a BigQuery routine.4 params▾

Check which of a given set of IAM permissions the caller has on a BigQuery routine.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the routine

permissionsarrayrequiredThe set of IAM permissions to check

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

routine_idstringrequiredThe ID of the routine to test permissions against

bigquery_test_row_access_policy_iam_permissions#Check which of a given set of IAM permissions the caller has on a row access policy.5 params▾

Check which of a given set of IAM permissions the caller has on a row access policy.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

permissionsarrayrequiredThe set of IAM permissions to check

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

row_access_policy_idstringrequiredThe ID of the row access policy to test permissions against

table_idstringrequiredThe ID of the table containing the row access policy

bigquery_test_table_iam_permissions#Check which of a given set of IAM permissions the caller has on a BigQuery table or view. This is a read-only check despite being a POST request — no state is modified.4 params▾

Check which of a given set of IAM permissions the caller has on a BigQuery table or view. This is a read-only check despite being a POST request — no state is modified.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

permissionsarrayrequiredThe set of IAM permissions to check

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table to test permissions against

bigquery_undelete_dataset#Restore a recently deleted BigQuery dataset. Undeletion is only possible for a short retention window after deletion.3 params▾

Restore a recently deleted BigQuery dataset. Undeletion is only possible for a short retention window after deletion.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the deleted dataset to restore

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

creation_timestringoptionalThe original dataset's creationTime timestamp, used to disambiguate which deleted generation to restore

bigquery_update_dataset#Update metadata for an existing BigQuery dataset, such as its friendly name, description, default table expiration, or labels.7 params▾

Update metadata for an existing BigQuery dataset, such as its friendly name, description, default table expiration, or labels.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset to update

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

default_partition_expiration_msintegeroptionalDefault lifetime in milliseconds for partitions in partitioned tables created in this dataset

default_table_expiration_msintegeroptionalDefault lifetime in milliseconds for tables created in this dataset

descriptionstringoptionalA description of the dataset

friendly_namestringoptionalA human-readable display name for the dataset

labelsobjectoptionalKey-value labels to attach to the dataset, e.g. {"env": "prod"}

bigquery_update_model#Update metadata for an existing BigQuery ML model, such as its friendly name, description, expiration time, or labels.7 params▾

Update metadata for an existing BigQuery ML model, such as its friendly name, description, expiration time, or labels.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the model

model_idstringrequiredThe ID of the model to update

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

descriptionstringoptionalA description of the model

expiration_timestringoptionalExpiration time for the model, in milliseconds since the epoch, as a string

friendly_namestringoptionalA human-readable display name for the model

labelsobjectoptionalKey-value labels to attach to the model, e.g. {"env": "prod"}

bigquery_update_routine#Replace the definition of an existing BigQuery routine (stored procedure or UDF). This is a full-replace operation — the complete routine definition must be supplied.9 params▾

Replace the definition of an existing BigQuery routine (stored procedure or UDF). This is a full-replace operation — the complete routine definition must be supplied.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the routine

definition_bodystringrequiredThe SQL body of the function or procedure

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

routine_idstringrequiredThe ID of the routine to update

routine_typestringrequiredThe type of routine

argumentsarrayoptionalThe list of input/output arguments for the routine

descriptionstringoptionalA description of the routine

languagestringoptionalThe language of the routine body

return_typeobjectoptionalThe return type of the routine, as a StandardSqlDataType object

bigquery_update_row_access_policy#Full replace of an existing row access policy on a BigQuery table (PUT semantics — rowAccessPolicies has no separate patch method, only this full-replace update, matching bigquery_update_routine's pattern). Both filter_predicate and grantees must be supplied.6 params▾

Full replace of an existing row access policy on a BigQuery table (PUT semantics — rowAccessPolicies has no separate patch method, only this full-replace update, matching bigquery_update_routine's pattern). Both filter_predicate and grantees must be supplied.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

filter_predicatestringrequiredA SQL boolean expression restricting which rows are visible to the grantees

granteesarrayrequiredThe principals this row access policy grants row visibility to

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

row_access_policy_idstringrequiredThe ID of the row access policy to update

table_idstringrequiredThe ID of the table containing the row access policy

bigquery_update_table#Update metadata for an existing BigQuery table, such as its schema (e.g. adding columns), description, friendly name, labels, or expiration time.8 params▾

Update metadata for an existing BigQuery table, such as its schema (e.g. adding columns), description, friendly name, labels, or expiration time.

NameTypeRequiredDescription

dataset_idstringrequiredThe ID of the dataset containing the table

project_idstringrequiredThe Google Cloud project ID that owns this BigQuery resource.

table_idstringrequiredThe ID of the table to update

descriptionstringoptionalA description of the table

expiration_timestringoptionalThe time when this table expires, in milliseconds since the epoch, as a string

friendly_namestringoptionalA human-readable display name for the table

labelsobjectoptionalKey-value labels to attach to the table, e.g. {"env": "prod"}

schemaobjectoptionalThe updated table schema, as the BigQuery Table.schema resource: an object with a 'fields' array, e.g. {"fields":[{"name":"col1","type":"STRING"}]}