Can Metorial connect Bigquery to AI agents?

Yes. Metorial connects AI agents to Bigquery through a governed integration layer, so teams can use the provider while keeping access controlled and observable.

Does the Bigquery integration work with MCP?

Metorial is MCP compatible and lets teams expose approved provider tools to MCP-capable agents and clients through a controlled access layer.

How does Metorial control access to Bigquery?

Metorial applies policies across users, groups, providers, agents, and individual tools, then records the context around every agent interaction.

Can teams trace Bigquery activity from agents?

Yes. Metorial records provider activity so teams can inspect tool calls, troubleshoot integrations, and give security teams the visibility they need.

Connect Bigquery to AI agents

get_job

Get Job

Retrieve detailed information about a specific BigQuery job including its configuration, status, and execution statistics.

list_jobs

List Jobs

List BigQuery jobs in the project. Jobs include queries, loads, exports, and copy operations. Filter by state, time range, or parent job.

list_routines

List Routines

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

delete_routine

Delete Routine

Permanently delete a BigQuery routine (UDF, procedure, or TVF). This action is irreversible.

create_routine

Create Routine

Create a new BigQuery routine: user-defined function (UDF), stored procedure, or table-valued function. Routines can be written in SQL or JavaScript.

cancel_job

Cancel Job

Cancel a running BigQuery job. The cancellation is best-effort; the job may still complete before the cancellation takes effect.

delete_dataset

Delete Dataset

Delete a BigQuery dataset. By default, the dataset must be empty. Set **deleteContents** to true to also delete all tables and views within it.

copy_table

Copy Table

Copy a BigQuery table to another table, within the same dataset or across datasets and projects. Creates an asynchronous copy job.

list_datasets

List Datasets

List all datasets in the configured BigQuery project. Returns dataset IDs, friendly names, locations, and labels. Use the **filter** parameter to narrow results.

export_data

Export Table to Cloud Storage

Export a BigQuery table to Google Cloud Storage as CSV, JSON, or Avro. Creates an asynchronous extract job. Use wildcards in the destination URI for sharded exports of large tables (e.g., gs://bucket/file-*.csv).

update_table

Update Table

Update a BigQuery table's metadata including friendly name, description, schema (add new columns), expiration, and labels.

read_table_data

Read Table Data

Read rows directly from a BigQuery table without running a query job. Useful for quickly inspecting table contents. For complex filtering or aggregation, use **Execute SQL Query** instead.

update_dataset

Update Dataset

Update an existing BigQuery dataset's metadata, including its friendly name, description, labels, and default expiration settings.

execute_query

Execute SQL Query

Run a GoogleSQL (standard SQL) query against BigQuery. Supports SELECT, DML (INSERT, UPDATE, DELETE, MERGE), and DDL (CREATE, ALTER, DROP) statements. The query is submitted as a job, polled for completion, and results are returned. Parameterized queries are supported for safe value interpolation. You can optionally write results to a destination table.

get_table

Get Table

Retrieve detailed metadata for a BigQuery table, including its schema, row count, size, partitioning configuration, and clustering settings.

list_tables

List Tables

List all tables, views, and materialized views in a BigQuery dataset. Returns table IDs, types, creation times, and expiration info.

delete_table

Delete Table

Permanently delete a BigQuery table or view. This action is irreversible.

create_dataset

Create Dataset

Create a new BigQuery dataset. A dataset is a top-level container for tables, views, and routines. Once created, its location cannot be changed.

insert_rows

Insert Rows (Streaming)

Stream rows into a BigQuery table using the streaming insert API. Rows are available for querying almost immediately. Each row is a JSON object matching the table schema. Optionally provide an insertId per row for best-effort deduplication.

load_data

Load Data from Cloud Storage

Load data from Google Cloud Storage into a BigQuery table. Supports CSV, JSON (newline-delimited), Avro, Parquet, ORC, Datastore, and Firestore export formats. Creates an asynchronous load job and returns the job status.

get_routine

Get Routine

Retrieve detailed information about a specific BigQuery routine including its definition, arguments, return type, and language.

get_dataset

Get Dataset

Retrieve detailed information about a specific BigQuery dataset, including its schema, access controls, creation time, and configuration.

create_table

Create Table

Create a new BigQuery table, view, or materialized view. Supports defining schemas with nested/repeated fields, time or range partitioning, and clustering. To create a view, provide the **viewQuery** parameter; for a materialized view, provide **materializedViewQuery**.

update_model

Update Model

Update BigQuery ML model metadata such as friendly name, description, expiration time, and labels. To change model training or prediction behavior, run SQL with **execute_query** instead.

delete_model

Delete Model

Permanently delete a BigQuery ML model from a dataset. This deletes the model resource and cannot be undone.

get_model

Get Model

Retrieve detailed metadata for a BigQuery ML model, including model type, feature columns, label columns, training runs, labels, and expiration metadata.

list_models

List Models

List BigQuery ML models in a dataset. Model creation and training are performed with SQL using **execute_query** and CREATE MODEL; this tool lists the resulting model resources and metadata.