Categories: FAANG

Forecasts and data insights come to BigQuery’s MCP and Agent Development Kit tools

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For AI agents to be really useful, they need to be able to securely interact with enterprise data. In July, we introduced a toolset to help AI agents interact with and analyze business data in BigQuery through natural language, and with just a few lines of code. Today, we’re taking the next step, with “Ask data insights” for Conversational Analytics and the “BigQuery Forecast” for time-series predictions, going beyond fetching metadata and executing queries to full-scale data analysis and predictions. Both tools are available today in the MCP Toolbox as well as Agent Development Kit’s built-in toolset.

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:

And here is the agent code:

code_block: <ListValue: [StructValue([(‘code’, ‘import asynciornfrom google.adk.agents import Agentrnfrom google.adk.runners import Runnerrnfrom google.adk.sessions import InMemorySessionServicernfrom google.adk.tools.bigquery import BigQueryCredentialsConfigrnfrom google.adk.tools.bigquery import BigQueryToolsetrnfrom google.adk.tools.bigquery.config import BigQueryToolConfigrnfrom google.adk.tools.bigquery.config import WriteModernfrom google.genai import typesrnfrom toolbox_core import ToolboxSyncClientrnimport google.authrnrn# Constants for this example agentrnAGENT_NAME = “bigquery_agent”rnAPP_NAME = “bigquery_app”rnUSER_ID = “user1234″rnSESSION_ID = “1234”rnGEMINI_MODEL = “gemini-2.5-pro”rnrn# A tool configuration to block any write operationsrntool_config = BigQueryToolConfig(write_mode=WriteMode.BLOCKED)rnrn# We are using application default credentialsrnapplication_default_credentials, _ = google.auth.default()rncredentials_config = BigQueryCredentialsConfig(rn credentials=application_default_credentialsrn)rnrn# Instantiate the built in BigQuery toolset with single toolrn# Use “ask_data_insights” for deeper insightsrnbigquery_toolset = BigQueryToolset(rn credentials_config=credentials_config, bigquery_tool_config=tool_config, tool_filter=[‘ask_data_insights’]rn)rnrn# Instantiate a Toolbox toolset. Only forecasting tool used. Make sure the Toolbox MCP server is already running locally. You can learn more at this codelabrntoolbox = ToolboxSyncClient(“http://127.0.0.1:5000”)rnmcp_tools = toolbox.load_toolset(‘bq-mcp-toolset’)rnrn# Agent Definitionrnroot_agent = Agent(rn model=GEMINI_MODEL,rn name=AGENT_NAME,rn description=(rn “Agent to answer questions about Google Analytics data stored in BigQuery”rn ),rn instruction=”””\rn You are a Google Analytics agent that can answer questions on Google Analytics data. rnrntImportant context: rnttYou have access to Google Analytics data in BigQuery which you should use to answer the questionsrnttTables available to you are – rntt1. `<my-project>.google_analytics_sample.daily_total_visits`rntt2. `<my-project>.google_analytics_sample.ga_sessions_20170801`rntAutomatically choose the table based on the user question. When either can be used, use the first one. rn “””,rn tools=mcp_tools+[bigquery_toolset],rn)’), (‘language’, ‘lang-py’), (‘caption’, <wagtail.rich_text.RichText object at 0x7fdb223d60a0>)])]>

Using the agent code above, let’s turn to the ADK’s developer UI, i.e., adk web, to test the agent and see it in action.

First, let’s use the tools to understand our data…

Agent using the insights tool to summarize the data

Then, let’s see if the agent can answer a business question.

The Conversational Analytics API backend is equipped with deeper thinking, and is able to bring out rich insights.

As you can see above, the Conversational Analytics API is equipped with the ability to perform deep thinking, so it can provide rich insights into our question.

Now, let’s see if the agent can predict the future.

Short answer, yes, yes it can, with a 95% confidence level. With these tools, the power of the TimesFM model is finally available to business users, regardless of their technical skill level.

Bring analysis and forecasts to your data

These new BigQuery capabilities will help developers reimagine how they build data-driven applications and agents. Together, we believe the combination of AI-powered Conversational Analytics and powerful, built-in forecasting capabilities will make performing sophisticated data analysis easier than ever.

Learn more about the ask_data_insights and BigQuery Forecast tools in the MCP Toolbox for databases and the core Agent Development Kit.

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