bigquery-mcp-data-agent
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@bigquery-mcp-data-agentFind the top 5 busiest Citi Bike stations by trip count."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
BigQuery MCP Data Agent
An AI-powered data analysis agent built with Google's Agent Development Kit (ADK), Gemini, BigQuery MCP Server, and Cloud Run.
Overview
This project demonstrates how an AI agent can connect to structured data in BigQuery through the Model Context Protocol (MCP).
The agent analyzes the NYC Citi Bike dataset and can answer analytical questions by inspecting the dataset, generating SQL queries, and retrieving data from BigQuery.
Related MCP server: BigQuery MCP
Technologies
Google Agent Development Kit (ADK)
Gemini
BigQuery
BigQuery MCP Server
Model Context Protocol (MCP)
Google Cloud Run
Python
Dataset
The agent works with the public NYC Citi Bike dataset:
bigquery-public-data.new_york_citibike
The dataset contains information about Citi Bike trips and stations in New York City.
How It Works
The user asks a question about the Citi Bike data.
Gemini analyzes the question.
The ADK agent uses BigQuery MCP tools to inspect the dataset.
The agent generates and executes read-only SQL queries.
BigQuery returns the requested data.
Gemini analyzes the results and provides an answer.
MCP Tools Used
The agent uses the following BigQuery MCP tools:
get_dataset_infolist_table_idsget_table_infoexecute_sql_readonly
Example Question
We have budget for 3 coffee trucks.
We want to find the best city bike stations to place our coffee trucks.This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
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