How do I install the bq mcp server?

Use a recorded configuration for your client, replace example credential values and paths, and verify the documented runtime. Restart or reconnect the client, then inspect the available tools.

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Bq MCP Server

This is a Python-based MCP (Model Context Protocol) server that retrieves dataset, table, and schema information from Google Cloud BigQuery, caches

Local serverstdioPython

What is the Bq MCP Server?

This is a Python-based MCP (Model Context Protocol) server that retrieves dataset, table, and schema information from Google Cloud BigQuery, caches it locally, and

What you get

Metadata Management — Retrieves and caches information about BigQuery datasets, tables, and columns
Keyword Search — Supports keyword search of cached metadata
Secure Query Execution — Provides SQL execution capabilities with automatic LIMIT clause insertion and cost control
File Export — Execute queries and save results to local files in CSV or JSONL format
MCP Compliance — Offers tools via the Model Context Protocol

Configuration and credentials

Python 3.11 or later - Google Cloud Platform account - GCP project with BigQuery API enabled

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

Maintainer setup documentation.

Recorded tools

Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.

Tool	What it does
save_query_result	The save_query_result tool provides advanced query execution with file export capabilities:

How to install the Bq MCP Server

claude mcp add bq -- uvx bq_mcp_server

Configuration

Python 3.11 or later - Google Cloud Platform account - GCP project with BigQuery API enabled

Example prompts to try

“Use Bq MCP to save query result.”
“Use Bq MCP to Prerequisites.”

Frequently asked questions

This is a Python-based MCP (Model Context Protocol) server that retrieves dataset, table, and schema information from Google Cloud BigQuery, caches it locally, and Confirm the tools offered by your installed version in the connected client. Tool availability and permissions depend on the server and account configuration.