get_job
Get Job
Retrieve detailed information about a specific BigQuery job including its configuration, status, and execution statistics.
Ask your agent in plain language. Each of these maps to a real tool on the server.
BigQuery is where analytics questions get answered. The integration lets an agent explore datasets, run SQL and return results, with a read-only query tool for teams that want analysis without the ability to change data.
Turn a question into SQL against your datasets and return the result with the query used.
List datasets and tables and read table schemas before writing a query, so the agent uses real column names.
Load files from Cloud Storage into a table, or export a result table back to a bucket.
Check running jobs, cancel an expensive query and review recent job history.
BigQuery accepts Google OAuth or a service account. With a service account, its IAM roles decide what the agent can do, so grant BigQuery Data Viewer and Job User for analysis and keep write roles off unless you need them. Give read-only teams the Execute Read-only SQL Query tool, not the full query tool.
Sign-in options on Metorial: Google OAuth, Service Account.
get_job
Retrieve detailed information about a specific BigQuery job including its configuration, status, and execution statistics.
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 user-defined functions (UDFs), stored procedures, and table-valued functions in a BigQuery dataset.
delete_routine
Permanently delete a BigQuery routine (UDF, procedure, or TVF). This action is irreversible.
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 a running BigQuery job. The cancellation is best-effort; the job may still complete before the cancellation takes effect.
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 a BigQuery table to another table, within the same dataset or across datasets and projects. Creates an asynchronous copy job.
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 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 a BigQuery table's metadata including friendly name, description, schema (add new columns), expiration, and labels.
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 an existing BigQuery dataset's metadata, including its friendly name, description, labels, and default expiration settings.
execute_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
Retrieve detailed metadata for a BigQuery table, including its schema, row count, size, partitioning configuration, and clustering settings.
list_tables
List all tables, views, and materialized views in a BigQuery dataset. Returns table IDs, types, creation times, and expiration info.
delete_table
Permanently delete a BigQuery table or view. This action is irreversible.
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
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 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
Retrieve detailed information about a specific BigQuery routine including its definition, arguments, return type, and language.
get_dataset
Retrieve detailed information about a specific BigQuery dataset, including its schema, access controls, creation time, and configuration.
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 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
Permanently delete a BigQuery ML model from a dataset. This deletes the model resource and cannot be undone.
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 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.
execute_sql_readonly
Run a read-only GoogleSQL query against BigQuery. The exact SQL is dry-run first and executes only when BigQuery classifies it as a SELECT statement. Scripts, procedures, DML, and DDL are rejected.
get_current_user
Get the connected Google account ID, email address, name, and profile image when available.
Every client uses the same Magic MCP URL, and so does any other MCP client. Add it once and sign in with SSO.
{"mcpServers": {"metorial": {"url": "<your-magic-mcp-url>"}}}
claude mcp add --transport http metorial <your-magic-mcp-url>
codex mcp add metorial --url <your-magic-mcp-url>codex mcp login metorial
One governed layer for every app your agents reach, not a separate connector per tool.
Execute SQL queries against BigQuery datasets, including DML and DDL statements. Create, update, list, and delete datasets and tables with support for partitioning, clustering, and nested fields. Load data from local files or Cloud Storage in formats like CSV, JSON, Avro, and Parquet, and stream data in real time. Export table data to Cloud Storage. Manage asynchronous jobs for queries, loads, exports, and copies. Query external data sources including Cloud Storage, Bigtable, Spanner, and Google Sheets via federated queries. Create and manage machine learning models using BigQuery ML with SQL. Define and manage scheduled queries and data transfer pipelines. Share datasets across organizations via Analytics Hub. Manage IAM permissions and data policies for column-level security. Create user-defined functions and stored procedures. Monitor BigQuery activity through audit log events routed to Pub/Sub.
Common questions about connecting Bigquery to AI agents with Metorial.