googleapis--mcp-toolbox/docs/en/integrations/bigquery/tools/bigquery-analyze-contribution.md

title, type, weight, description

title	type	weight	description
bigquery-analyze-contribution	docs	1	A "bigquery-analyze-contribution" tool performs contribution analysis in BigQuery.

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:

• allowed (default) and blocked: These modes do not impose any special restrictions on the bigquery-analyze-contribution tool.
• protected: This mode enables session-based execution. The tool will operate within the same BigQuery session as other tools using the same source. This allows the input_data parameter to be a query that references temporary resources (e.g., TEMP tables) created within that session.

The tool's behavior is also influenced by the allowedDatasets restriction on the bigquery source:

• Without allowedDatasets restriction: The tool can use any table or query for the input_data parameter.
• With allowedDatasets restriction: The tool verifies that the input_data parameter only accesses tables within the allowed datasets.
  • If input_data is a table ID, the tool checks if the table's dataset is in the allowed list.
  • If input_data is a query, the tool performs a dry run to analyze the query and rejects it if it accesses any table outside the allowed list.

Compatible Sources

{{< compatible-sources >}}

Example

kind: tool
name: contribution_analyzer
type: bigquery-analyze-contribution
source: my-bigquery-source
description: Use this tool to run contribution analysis on a dataset in BigQuery.

Reference

field	type	required	description
type	string	true	Must be "bigquery-analyze-contribution".
source	string	true	Name of the source the tool should execute on.
description	string	true	Description of the tool that is passed to the LLM.

Advanced Usage

Sample Prompt

You can prepare a sample table following https://cloud.google.com/bigquery/docs/get-contribution-analysis-insights. And use the following sample prompts to call this tool:

• What drives the changes in sales in the table bqml_tutorial.iowa_liquor_sales_sum_data? Use the project id myproject.
• Analyze the contribution for the total_sales metric in the table bqml_tutorial.iowa_liquor_sales_sum_data. The test group is identified by the is_test column. The dimensions are store_name, city, vendor_name, category_name and item_description.