Bigquery MCP server

Connect Bigquery to Claude, ChatGPT, Cursor, Claude Code, Codex, Gemini, GitHub Copilot and any other MCP client, for your whole team. Metorial adds security, governance, and tracing, and gives everyone one Magic MCP URL to connect.

What you can do with the Bigquery MCP server

Ask your agent in plain language. Each of these maps to a real tool on the server.

  • Use the get job tool in Bigquery
  • Use the list jobs tool in Bigquery
  • Use the list routines tool in Bigquery
  • Use the delete routine tool in Bigquery
  • Use the create routine tool in Bigquery
  • Use the cancel job tool in Bigquery

How teams use Bigquery with agents

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.

Ask the warehouse

Turn a question into SQL against your datasets and return the result with the query used.

Schema discovery

List datasets and tables and read table schemas before writing a query, so the agent uses real column names.

Load and export

Load files from Cloud Storage into a table, or export a result table back to a bucket.

Job housekeeping

Check running jobs, cancel an expensive query and review recent job history.

Tools to start with

  • Execute Read-only SQL Query: Analysis without DML or DDL.
  • Get Table: Returns the schema, including nested fields.
  • Load Data from Cloud Storage: Bulk loads CSV, JSON, Avro or Parquet.

Sign-in and permissions

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.

Supported Tools

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.

execute_sql_readonly

Execute Read-only SQL Query

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 Current User

Get the connected Google account ID, email address, name, and profile image when available.

Connect Bigquery to your AI client

Every client uses the same Magic MCP URL, and so does any other MCP client. Add it once and sign in with SSO.

  1. Enable Bigquery in Metorial. In your Metorial portal, enable the Bigquery MCP server and sign in with the account that owns the data. Access stays governed by your team policies.
  2. Add a custom connector. Open Customize, then Connectors. Select Add, then Custom, then Web.
  3. Paste your Metorial MCP URL. Name it Metorial, paste your Magic MCP URL (<your-magic-mcp-url>) and continue.
  4. Sign in. Select Add and sign in to load Bigquery tools.

Run Bigquery through the Metorial gateway

One governed layer for every app your agents reach, not a separate connector per tool.

  • Give Bigquery and every other app to your whole team through one MCP gateway, behind your SSO.
  • Scope which teams and agents can use Bigquery tools with role-based access control, and trace every call.
  • Prefer to run it yourself? Metorial is open source and self-hostable.

More integrations teams use with Bigquery

Google Sheets

Create, read, and update spreadsheets and their cell data. Read and write values to individual cells, ranges, or multiple ranges using A1 notation or named ranges. Apply cell formatting including text styles, backgrounds, borders, number formats, and conditional formatting. Manage sheets (tabs) within a spreadsheet—add, delete, copy, rename, and reorder them. Create and manage embedded charts, pivot tables, filter views, data validation rules, protected ranges, named ranges, and merged cells. Perform batch operations to apply multiple updates atomically.

GitHub

Manage repositories, issues, and pull requests. Create and configure branches, star repositories, review code, and merge changes. Automate CI/CD workflows with GitHub Actions, manage workflow runs, secrets, and artifacts. Track issues with labels, milestones, and assignees. Search across code, repositories, issues, and users. Manage organizations, teams, and memberships. Create and manage projects, gists, packages, deployments, and environments. Access security alerts including code scanning, secret scanning, and Dependabot alerts. Read and write file contents in repositories. Manage webhooks, notifications, and codespaces.

Salesforce

Manage CRM data including Accounts, Contacts, Leads, Opportunities, Cases, and custom objects. Create, read, update, and delete records. Query data using SOQL and search across objects using SOSL. Perform bulk data operations for large-scale imports, exports, and migrations. Execute composite requests to batch multiple operations in a single API call. Access analytics, reports, and dashboards. Manage files and attachments associated with records. Interact with Chatter feeds, posts, and groups for social collaboration. Manage org metadata including custom objects, fields, layouts, and workflows. Query data using GraphQL for precise data retrieval across related objects.

Sharepoint

Manage SharePoint sites, document libraries, lists, and files. Create, read, update, and delete lists and list items with custom columns. Resolve site users to numeric Person/Group LookupId values. Upload, download, move, copy, and version files in document libraries. Search across sites, files, folders, lists, and list items using Microsoft Search. Manage permissions at site, list, and item levels with granular access control. Define and manage content types and site columns. Retrieve site properties and search for sites across Microsoft 365.

Airtable

Create, read, update, and delete records in Airtable bases and tables. Manage base schemas including creating tables and fields. Filter records using formulas, sort by fields, and scope queries to specific views. Upsert records to find, create, or update in a single call. Upload attachments to records, read and write record comments, list accessible bases, and receive real-time base change events through webhooks.

Google Drive

Upload, download, create, copy, move, rename, trash, and permanently delete files and folders in Google Drive. Search for files using complex queries filtering by name, MIME type, owner, modification date, labels, and other metadata. Share files and folders with specific users, groups, or domains with role-based permissions (owner, writer, commenter, reader). Manage shared drives and their members. Export Google Workspace files (Docs, Sheets, Slides) to standard formats like PDF, DOCX, and XLSX. Track file revision history and restore earlier versions. Create, read, update, and delete threaded comments and replies on files. Read label definitions and search files by label. Browse the Drive apps installed for your account. Store app-specific data in a hidden per-user folder.

Technical notes for Bigquery

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.

Frequently asked questions

Common questions about connecting Bigquery to AI agents with Metorial.

  1. How many tools does the Bigquery MCP server have?
    The Bigquery MCP server exposes 29 tools, including Get Job, List Jobs, List Routines. Each one is governed and traced through Metorial.
  2. Bigquery exposes tools your agents call on demand; it does not publish event triggers yet. Everything still runs through one governed MCP URL.
  3. Any MCP client, including VS Code, Windsurf, Zed, OpenCode and Grok. Each one reaches the Bigquery MCP server through the same Metorial URL.
  4. Use role-based access control to decide which teams, users, and agents can use Bigquery and which of its tools they can call.
  5. Yes. Metorial is open source, so you can run the Bigquery MCP server yourself or use the hosted gateway.

Connect Bigquery to production AI agents

See how Metorial gives your team governed access to Bigquery, with tracing and security controls built in.