Files
google--adk-python/contributing/samples/bigquery_mcp
Wei (Jack) Sun 1c64a41b53 feat: ADK 2.0 alpha
Introduces two major capabilities:
- Workflow runtime: graph-based execution engine for composing
  deterministic execution flows for agentic apps, with support for
  routing, fan-out/fan-in, loops, retry, state management, dynamic
  nodes, human-in-the-loop, and nested workflows
- Task API: structured agent-to-agent delegation with multi-turn
  task mode, single-turn controlled output, mixed delegation
  patterns, human-in-the-loop, and task agents as workflow nodes

Co-Authored-By: Bo Yang <ybo@google.com>
Co-Authored-By: George Weale <gweale@google.com>
Co-Authored-By: Sean Zhou <seanzhougoogle@google.com>
Co-Authored-By: Shangjie Chen <deanchen@google.com>
Co-Authored-By: Swapnil Agarwal <swapnilag@google.com>
Co-Authored-By: Wei Sun <weisun@google.com>
Co-Authored-By: Xuan Yang <xygoogle@google.com>
Co-Authored-By: Yifan Wang <wanyif@google.com>
Change-Id: I35932c50cfe29ff68559e3781713dbb5eb7b3382
2026-03-17 23:24:58 -07:00
..
2026-03-17 23:24:58 -07:00
2026-03-17 23:24:58 -07:00
2026-01-20 14:21:01 -08:00

BigQuery MCP Toolset Sample

Introduction

This sample agent demonstrates using ADK's McpToolset to interact with BigQuery's official MCP endpoint, allowing an agent to access and execute tools by leveraging the Model Context Protocol (MCP). These tools include:

  1. list_dataset_ids

Fetches BigQuery dataset ids present in a GCP project.

  1. get_dataset_info

Fetches metadata about a BigQuery dataset.

  1. list_table_ids

Fetches table ids present in a BigQuery dataset.

  1. get_table_info

Fetches metadata about a BigQuery table.

  1. execute_sql

Runs or dry-runs a SQL query in BigQuery.

How to use

Set up your project and local authentication by following the guide Use the BigQuery remote MCP server. This agent uses Application Default Credentials (ADC) to authenticate with the BigQuery MCP endpoint.

Set up environment variables in your .env file for using Google AI Studio or Google Cloud Vertex AI for the LLM service for your agent. For example, for using Google AI Studio you would set:

  • GOOGLE_GENAI_USE_VERTEXAI=FALSE
  • GOOGLE_API_KEY={your api key}

Then run the agent using adk run . or adk web . in this directory.

Sample prompts

  • which weather datasets exist in bigquery public data?
  • tell me more about noaa_lightning
  • which tables exist in the ml_datasets dataset?
  • show more details about the penguins table
  • compute penguins population per island.