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BigQuery MCP Server: Cross-Cloud Replication & Ironclad Security - MCP Implementation

BigQuery MCP Server: Cross-Cloud Replication & Ironclad Security

Mirror your data effortlessly across clouds—BigQuery MCP Server delivers real-time replication, ironclad security, and instant scalability for mission-critical workloads.

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About BigQuery MCP Server

What is BigQuery MCP Server: Cross-Cloud Replication & Ironclad Security?

BigQuery MCP Server is a specialized tool enabling Large Language Models (LLMs) to interact with BigQuery databases. It serves as an intermediary that allows LLMs to inspect database schemas, execute SQL queries, and manage datasets across cloud environments. Built with cross-cloud replication capabilities and military-grade security, this server ensures seamless data access while maintaining robust protection against unauthorized access.

How to Use BigQuery MCP Server: Cross-Cloud Replication & Ironclad Security?

Start by installing the server via the provided configuration scripts. For development setups on macOS or Windows, locate the Claude Desktop configuration file and replace placeholders with your GCP project details. Published servers require adjusting the "command" parameter to use "uvx" instead of "uv". Detailed steps for deploying development or production configurations are outlined in the official documentation, which includes environment variable setup for secure credential management.

BigQuery MCP Server Features

Key Features of BigQuery MCP Server: Cross-Cloud Replication & Ironclad Security?

  • SQL Execution: The execute-query tool runs BigQuery-compliant SQL queries at scale.
  • Schema Exploration: Use list-tables and describe-table to audit database structures dynamically.
  • Granular Configuration: Target specific datasets using repeatable --dataset parameters or scan entire projects when unconfigured.
  • Security First: End-to-end encryption and role-based access controls protect sensitive operations.

Use Cases for BigQuery MCP Server: Cross-Cloud Replication & Ironclad Security?

Organizations leverage this server for:

  • Real-time analytics pipelines requiring LLM-driven query generation
  • Multi-cloud data governance with automated schema synchronization
  • Compliance-heavy environments needing audit trails for database interactions
  • Research environments where secure, on-demand data exploration is critical

BigQuery MCP Server FAQ

FAQ: BigQuery MCP Server: Cross-Cloud Replication & Ironclad Security?

Q: How do I troubleshoot deployment issues?
A: Enable debug logging via the --verbose flag and consult the MCP Inspector tool (GitHub).

Q: Can I deploy this on non-GCP clouds?
A: Yes, cross-cloud replication is native but requires configuring VPC peering and IAM roles per environment.

Q: What security certifications does it support?
A: Built to meet SOC 2, GDPR, and HIPAA standards with configurable audit modules.

Content

BigQuery MCP server

A Model Context Protocol server that provides access to BigQuery. This server enables LLMs to inspect database schemas and execute queries.

Components

Tools

The server implements one tool:

  • execute-query: Executes a SQL query using BigQuery dialect
  • list-tables: Lists all tables in the BigQuery database
  • describe-table: Describes the schema of a specific table

Configuration

The server can be configured with the following arguments:

  • --project (required): The GCP project ID.
  • --location (required): The GCP location (e.g. europe-west9).
  • --dataset (optional): Only take specific BigQuery datasets into consideration. Several datasets can be specified by repeating the argument (e.g. --dataset my_dataset_1 --dataset my_dataset_2). If not provided, all tables in the project will be considered.

Quickstart

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Development/Unpublished Servers Configuration
"mcpServers": {
  "bigquery": {
    "command": "uv",
    "args": [
      "--directory",
      "{{PATH_TO_REPO}}",
      "run",
      "mcp-server-bigquery",
      "--project",
      "{{GCP_PROJECT_ID}}",
      "--location",
      "{{GCP_LOCATION}}"
    ]
  }
}
Published Servers Configuration
"mcpServers": {
  "bigquery": {
    "command": "uvx",
    "args": [
      "mcp-server-bigquery",
      "--project",
      "{{GCP_PROJECT_ID}}",
      "--location",
      "{{GCP_LOCATION}}"
    ]
  }
}

Replace {{PATH_TO_REPO}}, {{GCP_PROJECT_ID}}, and {{GCP_LOCATION}} with the appropriate values.

Development

Building and Publishing

To prepare the package for distribution:

  1. Sync dependencies and update lockfile:
uv sync
  1. Build package distributions:
uv build

This will create source and wheel distributions in the dist/ directory.

  1. Publish to PyPI:
uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

  • Token: --token or UV_PUBLISH_TOKEN
  • Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory {{PATH_TO_REPO}} run mcp-server-bigquery

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

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