adk-python/contributing/samples/bigquery_mcp

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 toole by leveraging the Model Context Protocol (MCP). These tools include:

1. list_dataset_ids

Fetches BigQuery dataset ids present in a GCP project.

2. get_dataset_info

Fetches metadata about a BigQuery dataset.

3. list_table_ids

Fetches table ids present in a BigQuery dataset.

4. get_table_info

Fetches metadata about a BigQuery table.

5. 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.