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bigquery-mcp

bigquery-mcp

Local
github.com/PaddyAltonDatabases1Updated 81w ago
Repository

About bigquery-mcp

bigquery-mcp is an MCP server for AI Agents working with BigQuery. It exposes tools for inspecting datasets, tables, columns, and query history, supplying context for data analysis tasks.

Key features

  1. 01Inspect datasets and tables
  2. 02View column information
  3. 03Inspect query history
  4. 04Access data through the BigQuery client

Use cases

  1. 01Explore a warehouse's data structure
  2. 02Gather table and column context for SQL analysis
  3. 03Review BigQuery query history

MCP support

The MCP server exposes tools for AI Agents to inspect BigQuery warehouse contents, including datasets, tables, columns, and query history.

#bigquery#mcp#ai-agents

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

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

  1. ensure you have the prerequisites installed
  2. clone down this repository
  3. run uv sync to install the dependencies
  4. 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).

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.