AI-based forecasting and analytics in BigQuery via MCP and ADK

Let’s dive into what you can do with these new tools.

ask_data_insights: Converse with BigQuery

With the ask_data_insights tool, you can now answer complex questions of your structured data in BigQuery using plain English.

Built on top of the powerful Conversational Analytics API, ask_data_insights enables an agent to utilize the API to offload the task of understanding the user’s question, pulling in relevant context, formulating and executing the queries, and summarizing the answer in plain English. Along the way, the ask_data_insights tool shows its work, returning a detailed step-by-step log of its process, so you have full transparency into how it arrived at the answer.

Predict the future with BigQuery Forecast

Information without insights is just noise. The ability to predict future trends, whether sales, user traffic, or inventory needs, is critical for any business. BigQuery Forecast simplifies time-series forecasting using BigQuery ML’s AI.FORECAST function based on the built-in TimesFM model.

With BigQuery Forecast, the agent can run the forecasting job directly within BigQuery, without you having to set up machine learning infrastructure. Point the tool at your data, specify what you want to predict and a time horizon, and the agent will make its predictions using TimesFM.

New tools in action: Building a Google Analytics Data Agent

Let’s explore how to build a simple agent to answer questions about Google Analytics 360 data using ask_data_insights and BigQuery Forecast. For this demo,

• The data is stored in BigQuery tables. Users of this agent only require read access to these tables, which are available under the BigQuery public dataset. bigquery-public-data.google_analytics_sample.
• We will use ADK to build this agent and “adk web” to test it.
• We are using one tool from the ADK’s built-in tools and one from the MCP toolbox. You can choose to use either option depending on your agent architecture and needs.

This diagram shows the architecture of this simple agent: