Explore official Google BigQuery MCP servers. Find resources and examples to build context-aware apps in Google's ecosys
by google·Updated Apr 13, 2026Add new capabilities to Claude beyond text generation
Example: Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example: Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example: Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Time estimate: 15-60 minutes depending on server complexity
Prerequisites
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We wired Google BigQuery into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Google BigQuery is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Google BigQuery is the kind of server we cite when onboarding engineers to host + tool permissions.
Useful MCP listing: Google BigQuery is the kind of server we cite when onboarding engineers to host + tool permissions.
We evaluated Google BigQuery against two servers with overlapping tools; this profile had the clearer scope statement.
Google BigQuery reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Google BigQuery reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Google BigQuery is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We wired Google BigQuery into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
I recommend Google BigQuery for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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Select a client to integrate this server.
Claude Desktop configuration
{
"mcpServers": {
"BigQuery": {
"url": "https://bigquery.googleapis.com/mcp"
}
}
}Provides access to Google's BigQuery data warehouse through the Model Context Protocol, allowing AI agents to query and analyze large datasets directly.
Google BigQuery is an official MCP server published by google that provides AI assistants with tools and capabilities via the Model Context Protocol. Explore official Google BigQuery MCP servers. Find resources and examples to build context-aware apps in Google's ecosys It is categorized under analytics data.
You can install Google BigQuery in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
google/mcpThis repository contains a list of Google's official Model Context Protocol (MCP) servers, guidance on how to deploy MCP servers to Google Cloud, and examples to get started.
These remote MCP servers are managed by Google, and are available via endpoint. This list will be kept up-to-date as more remote servers become available.
You can run these open-source MCP servers locally, or deploy them to Google Cloud (see below).
/examples/launchmybakery): A sample agent built with Agent Development Kit (ADK) that uses remote MCP servers for Google Maps and BigQuery.We welcome contributions to this repository, including bug reports, feature requests, documentation improvements, and code contributions. Please see our Contributing Guidelines to get started.
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
This is not an officially supported Google product. This project is intended for demonstration purposes only.
This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.