MCP Toolbox for Databases

--- title: "bigquery-analyze-contribution" type: docs weight: 1 description: > A "bigquery-analyze-contribution" tool performs contribution analysis in BigQuery. aliases: - /resources/tools/bigquery-analyze-contribution --- ## 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. It's compatible with the following sources: - [bigquery](../../sources/bigquery.md) `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. ## Example ```yaml tools: contribution_analyzer: kind: bigquery-analyze-contribution source: my-bigquery-source description: Use this tool to run contribution analysis on a dataset in BigQuery. ``` ## 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`. ## Reference | **field** | **type** | **required** | **description** | |-------------|:--------:|:------------:|------------------------------------------------------------| | kind | 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. |