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BigQuery MCP Server

by ergut

This server allows LLMs like Claude to directly interact with your BigQuery data. It acts as a translator between your AI assistant and your database, ensuring secure and efficient communication.

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What is BigQuery MCP Server?

The BigQuery MCP Server is a server that enables Large Language Models (LLMs) to query and interact with BigQuery data using natural language. It utilizes the Model Context Protocol (MCP) to facilitate communication between the LLM and the database.

How to use BigQuery MCP Server?

To use the server, you need to set up authentication with Google Cloud, configure your Claude Desktop config file with the server details (project ID, location, and optionally a service account key), and then start chatting with your data through Claude Desktop.

Key features of BigQuery MCP Server

  • Run SQL queries by asking questions in plain English

  • Access both tables and materialized views in your datasets

  • Explore dataset schemas with clear labeling of resource types

  • Analyze data within safe limits (1GB query limit by default)

  • Keep your data secure (read-only access)

Use cases of BigQuery MCP Server

  • Quickly retrieve insights from BigQuery data using natural language

  • Generate reports and visualizations based on spoken queries

  • Automate data analysis tasks with AI assistance

  • Enable non-technical users to access and explore data

FAQ from BigQuery MCP Server

What is MCP?

MCP stands for Model Context Protocol, a universal translator for AI-database communication.

Which LLMs are supported?

Currently, MCP support is only available in Claude Desktop (developer preview).

What are the limitations?

Connections are limited to local MCP servers, queries are read-only with a 1GB processing limit, and some complex view types might have limitations.

What permissions are needed?

You'll need either the roles/bigquery.user role or both roles/bigquery.dataViewer and roles/bigquery.jobUser roles.

How do I set up authentication?

You can authenticate using Google Cloud CLI or a service account key file.