MCP Reference: bigquery.googleapis.com

BigQuery MCP server provides tools to interact with BigQuery

A Model Context Protocol (MCP) server acts as a proxy between an external service that provides context, data, or capabilities to a Large Language Model (LLM) or AI application. MCP servers connect AI applications to external systems such as databases and web services, translating their responses into a format that the AI application can understand.

Server Setup

You must enable MCP servers and set up authentication before use. For more information about using Google and Google Cloud remote MCP servers, see Google Cloud MCP servers overview.

Server Endpoints

An MCP service endpoint is the network address and communication interface (usually a URL) of the MCP server that an AI application (the Host for the MCP client) uses to establish a secure, standardized connection. It is the point of contact for the LLM to request context, call a tool, or access a resource. Google MCP endpoints can be global or regional.

The BigQuery API MCP server has the following global MCP endpoint:

  • https://bigquery.googleapis.com/mcp

MCP Tools

An MCP tool is a function or executable capability that an MCP server exposes to a LLM or AI application to perform an action in the real world.

Tools

The bigquery.googleapis.com MCP server has the following tools:

MCP Tools
list_dataset_ids List BigQuery dataset IDs in a Google Cloud project. Supports pagination. Use page_size to limit results and page_token to retrieve next page.
get_dataset_info Get metadata information about a BigQuery dataset.
list_table_ids List table ids in a BigQuery dataset. Supports pagination. Use page_size to limit results and page_token to retrieve next page.
get_table_info Get metadata information about a BigQuery table.
execute_sql_readonly

Run a read-only SQL query in the project and return the result. Prefer this tool over execute_sql if possible.

This tool is restricted to only SELECT statements. INSERT, UPDATE, and DELETE statements and stored procedures aren't allowed. If the query doesn't include a SELECT statement, an error is returned. For information on creating queries, see the GoogleSQL documentation.

Example Queries:

-- Count the number of penguins in each island.
SELECT island, COUNT(*) AS population
FROM bigquery-public-data.ml_datasets.penguins GROUP BY island

-- Evaluate a bigquery ML Model.
SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`)

-- Evaluate BigQuery ML model on custom data
SELECT *
FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`))

-- Predict using BigQuery ML model:
SELECT *
FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`))

-- Forecast data using AI.FORECAST
SELECT *
FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips',
  timestamp_col => 'date', id_cols => ['usertype'], horizon => 30)

Queries executed using the execute_sql_readonly tool will always have the job label goog-mcp-server: true automatically set in addition to any custom labels provided in the request. Queries are charged to the project specified in the project_id field.
execute_sql

Run a SQL query in the project and return the result. Prefer the execute_sql_readonly tool if possible.

This tool can execute any query that bigquery supports including:

  • SQL Queries (SELECT, INSERT, UPDATE, DELETE, CREATE, etc.)
  • AI/ML functions like AI.FORECAST, ML.EVALUATE, ML.PREDICT
  • Any other query that bigquery supports.

Example Queries:

-- Insert data into a table.
INSERT INTO `my_project.my_dataset`.my_table (name, age)
VALUES ('Alice', 30);

-- Create a table.
CREATE TABLE `my_project.my_dataset`.my_table (
  name STRING,
  age INT64);

-- DELETE data from a table.
DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice';

-- Create Dataset
CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US');

-- Drop table
DROP TABLE `my_project.my_dataset`.my_table;

-- Drop dataset
DROP SCHEMA `my_project.my_dataset`;

-- Create Model
CREATE OR REPLACE MODEL `my_project.my_dataset.my_model`
OPTIONS (
  model_type = 'LINEAR_REG'
  LS_INIT_LEARN_RATE=0.15,
  L1_REG=1,
  MAX_ITERATIONS=5,
  DATA_SPLIT_METHOD='SEQ',
  DATA_SPLIT_EVAL_FRACTION=0.3,
  DATA_SPLIT_COL='timestamp') AS
SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`;

Queries executed using the execute_sql tool will always have the default job label goog-mcp-server: true automatically set in addition to any custom labels provided in the request. Queries are charged to the project specified in the project_id field.

Get MCP tool specifications

To get the MCP tool specifications for all tools in an MCP server, use the tools/list method. The following example demonstrates how to use curl to list all tools and their specifications currently available within the MCP server.

Curl Request 
curl --location 'https://bigquery.googleapis.com/mcp' \
--header 'content-type: application/json' \
--header 'accept: application/json, text/event-stream' \
--data '{
    "method": "tools/list",
    "jsonrpc": "2.0",
    "id": 1
}'