Tools on MCP Toolbox for Databases

bigquery-analyze-contribution

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-analyze-contribution tool performs contribution analysis in BigQuery by creating a temporary CONTRIBUTION_ANALYSIS model and then querying it with ML.GET_INSIGHTS to find top contributors for a given metric.

bigquery-analyze-contribution takes the following parameters:

input_data (string, required): The data that contain the test and control data to analyze. This can be a fully qualified BigQuery table ID (e.g., my-project.my_dataset.my_table) or a SQL query that returns the data.
contribution_metric (string, required): The name of the column that contains the metric to analyze. This can be SUM(metric_column_name), SUM(numerator_metric_column_name)/SUM(denominator_metric_column_name) or SUM(metric_sum_column_name)/COUNT(DISTINCT categorical_column_name) depending the type of metric to analyze.
is_test_col (string, required): The name of the column that identifies whether a row is in the test or control group. The column must contain boolean values.
dimension_id_cols (array of strings, optional): An array of column names that uniquely identify each dimension.
top_k_insights_by_apriori_support (integer, optional): The number of top insights to return, ranked by apriori support. Default to ‘30’.
pruning_method (string, optional): The method to use for pruning redundant insights. Can be 'NO_PRUNING' or 'PRUNE_REDUNDANT_INSIGHTS'. Defaults to 'PRUNE_REDUNDANT_INSIGHTS'.

The behavior of this tool is influenced by the writeMode setting on its bigquery source:

bigquery-conversational-analytics

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-conversational-analytics tool allows you to ask questions about your data in natural language.

This function takes a user’s question (which can include conversational history for context) and references to specific BigQuery tables, and sends them to a stateless conversational API.

The API uses a GenAI agent to understand the question, generate and execute SQL queries and Python code, and formulate an answer. This function returns a detailed, sequential log of this entire process, which includes any generated SQL or Python code, the data retrieved, and the final text answer.

bigquery-execute-sql

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-execute-sql tool executes a SQL statement against BigQuery.

bigquery-execute-sql accepts the following parameters:

sql (required): The GoogleSQL statement to execute.
dry_run (optional): If set to true, the query is validated but not run, returning information about the execution instead. Defaults to false.

The behavior of this tool is influenced by the writeMode setting on its bigquery source:

allowed (default): All SQL statements are permitted.
blocked: Only SELECT statements are allowed. Any other type of statement (e.g., INSERT, UPDATE, CREATE) will be rejected.
protected: This mode enables session-based execution. SELECT statements can be used on all tables, while write operations are allowed only for the session’s temporary dataset (e.g., CREATE TEMP TABLE ...). This prevents modifications to permanent datasets while allowing stateful, multi-step operations within a secure session.

The tool’s behavior is influenced by the allowedDatasets restriction on the bigquery source. Similar to writeMode, this setting provides an additional layer of security by controlling which datasets can be accessed:

bigquery-forecast

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-forecast tool forecasts time series data in BigQuery.

bigquery-forecast constructs and executes a SELECT * FROM AI.FORECAST(...) query based on the provided parameters:

history_data (string, required): This specifies the source of the historical time series data. It can be either a fully qualified BigQuery table ID (e.g., my-project.my_dataset.my_table) or a SQL query that returns the data.
timestamp_col (string, required): The name of the column in your history_data that contains the timestamps.
data_col (string, required): The name of the column in your history_data that contains the numeric values to be forecasted.
id_cols (array of strings, optional): If you are forecasting multiple time series at once (e.g., sales for different products), this parameter takes an array of column names that uniquely identify each series. It defaults to an empty array if not provided.
horizon (integer, optional): The number of future time steps you want to predict. It defaults to 10 if not specified.

The behavior of this tool is influenced by the writeMode setting on its bigquery source:

bigquery-get-dataset-info

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-get-dataset-info tool retrieves metadata for a BigQuery dataset.

bigquery-get-dataset-info accepts the following parameters:

dataset (required): Specifies the dataset for which to retrieve metadata.
project (optional): Defines the Google Cloud project ID. If not provided, the tool defaults to the project from the source configuration.

The tool’s behavior regarding these parameters is influenced by the allowedDatasets restriction on the bigquery source:

Without allowedDatasets restriction: The tool can retrieve metadata for any dataset specified by the dataset and project parameters.
With allowedDatasets restriction: Before retrieving metadata, the tool verifies that the requested dataset is in the allowed list. If it is not, the request is denied. If only one dataset is specified in the allowedDatasets list, it will be used as the default value for the dataset parameter.

Compatible Sources

This tool can be used with the following database sources:

bigquery-get-table-info

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-get-table-info tool retrieves metadata for a BigQuery table.

bigquery-get-table-info accepts the following parameters:

table (required): The name of the table for which to retrieve metadata.
dataset (required): The dataset containing the specified table.
project (optional): The Google Cloud project ID. If not provided, the tool defaults to the project from the source configuration.

The tool’s behavior regarding these parameters is influenced by the allowedDatasets restriction on the bigquery source:

bigquery-list-dataset-ids

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-list-dataset-ids tool returns all dataset IDs from the source.

bigquery-list-dataset-ids accepts the following parameter:

project (optional): Defines the Google Cloud project ID. If not provided, the tool defaults to the project from the source configuration.

The tool’s behavior regarding this parameter is influenced by the allowedDatasets restriction on the bigquery source:

Without allowedDatasets restriction: The tool can list datasets from any project specified by the project parameter.
With allowedDatasets restriction: The tool directly returns the pre-configured list of dataset IDs from the source, and the project parameter is ignored.

Compatible Sources

This tool can be used with the following database sources:

bigquery-list-table-ids

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-list-table-ids tool returns table IDs in a given BigQuery dataset.

bigquery-list-table-ids accepts the following parameters:

dataset (required): Specifies the dataset from which to list table IDs.
project (optional): Defines the Google Cloud project ID. If not provided, the tool defaults to the project from the source configuration.

The tool’s behavior regarding these parameters is influenced by the allowedDatasets restriction on the bigquery source:

Without allowedDatasets restriction: The tool can list tables from any dataset specified by the dataset and project parameters.
With allowedDatasets restriction: Before listing tables, the tool verifies that the requested dataset is in the allowed list. If it is not, the request is denied. If only one dataset is specified in the allowedDatasets list, it will be used as the default value for the dataset parameter.

Compatible Sources

This tool can be used with the following database sources:

bigquery-search-catalog

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-search-catalog tool returns all entries in Dataplex Catalog (e.g. tables, views, models) with system=bigquery that matches given user query.

bigquery-search-catalog takes a required query parameter based on which entries are filtered and returned to the user. It also optionally accepts following parameters:

datasetIds - The IDs of the bigquery dataset.
projectIds - The IDs of the bigquery project.
types - The type of the data. Accepted values are: CONNECTION, POLICY, DATASET, MODEL, ROUTINE, TABLE, VIEW.
pageSize - Number of results in the search page. Defaults to 5.

Compatible Sources

This tool can be used with the following database sources:

bigquery-sql

Mon, 01 Jan 0001 00:00:00 +0000

About

A bigquery-sql tool executes a pre-defined SQL statement.

The behavior of this tool is influenced by the writeMode setting on its bigquery source:

allowed (default) and blocked: These modes do not impose any restrictions on the bigquery-sql tool. The pre-defined SQL statement will be executed as-is.
protected: This mode enables session-based execution. The tool will operate within the same BigQuery session as other tools using the same source, allowing it to interact with temporary resources like TEMP tables created within that session.

Compatible Sources

This tool can be used with the following database sources: