Bigquery Mcp
An MCP server to help AI Agents inspect the contents of a BigQuery warehouse
Install
$ agentstack add mcp-paddyalton-bigquery-mcp ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README — it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/mcp-paddyalton-bigquery-mcp) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming — see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps — measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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 syncto 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).
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: PaddyAlton
- Source: PaddyAlton/bigquery-mcp
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.