bigquery-mcp

A Model Context Protocol (MCP) Server for BigQuery.

Prerequisites

This project and the mcp CLI rely on your having the dependency management tool uv installed. You can install via e.g. brew install uv for Homebrew users. See here for alternatives.

This project (currently) assumes you can 'transparently' create a BigQuery Client, which is usually the case if you have gcloud installed in your local environment. In other environments you may need suitable service account credentials (and you can set the GOOGLE_APPLICATION_CREDENTIALS environment variable to indicate the location of these credentials to the BigQuery client library).

For development

You will also need to have Taskfile installed. brew install go-task will work if you are a Homebrew user. See here for alternatives.

Quickstart for Cursor IDE

ensure you have the prerequisites installed
clone down this repository
run uv sync to install the dependencies
in Cursor settings > MCP Servers, start a server with the following command:

uv run --with mcp --directory /path/to/bigquery-mcp mcp run /path/to/bigquery-mcp/src/server.py

It is recommended to write a contextual rule in .cursor/rules/tool-use-rule.mdc into your working project. The Cursor Agent will need some instructions if it is to use the tools properly.

Details

AI Agents have begun to excel at writing code, but often struggle with data-related tasks. This is because of the coupling between programme logic and the actual contents of the database.

More specifically, AI Agents often fail to write good SQL queries for analysis tasks. They are capable of writing code, so the issue is not a lack of ability in this arena and more due to a lack of context about the contents of the database.

This MCP server assists with this problem area by providing AI Agents with tools they can use to examine the contents of a BigQuery data warehouse (i.e. datasets, tables, columns, query history).