이게 뭐야? 🤔

이 서버는 Claude 같은 LLM이 BigQuery 데이터와 직접 소통할 수 있게 해줍니다 — 읽기 전용으로, 창고를 변경할 수 없습니다. AI 어시스턴트와 데이터베이스 사이에 위치한 친절한 번역기라고 생각해보세요. 안전하고 효율적으로 채팅할 수 있도록 보장합니다.

빠른 예시

더 이상 손으로 SQL 쿼리를 작성하지 않고, 그냥 자연스럽게 데이터와 대화할 수 있습니다!

어떻게 작동하나요? 🛠️

이 서버는 모델 컨텍스트 프로토콜(MCP)을 사용하며, 이는 AI-데이터베이스 통신을 위한 범용 번역기와 같습니다. MCP는 Claude Desktop, Claude Code 및 점점 늘어나는 다른 AI 클라이언트에서 지원되고 있습니다.

필요한 것은 다음과 같습니다:

1. 인증 설정 (아래 참조)
2. 프로젝트 세부 정보를 MCP 클라이언트의 설정 파일에 추가하세요
3. BigQuery 데이터와 자연스럽게 대화를 시작하세요!

이게 뭘 할 수 있지? 📊

• 설계상 읽기 전용 — SELECT 문만 허용됩니다. 모든 쿼리는 실행 전에 BigQuery의 사전 계획 계획 프로그램에서 검증되므로, INSERT, UPDATE, DELETE, DROP, TRUNCATE, EXPORT DATA, MERGE 모두 거부됩니다. AI 에이전트는 당신의 창고를 돌연변이시킬 수 없습니다.
• 단순한 영어로 질문만 하여 SQL 쿼리를 실행하기

BigQuery MCP Server

What is this? 🤔

This is a server that lets your LLMs (like Claude) talk directly to your BigQuery data — read-only, with no ability to mutate your warehouse. Think of it as a friendly translator that sits between your AI assistant and your database, making sure they can chat securely and efficiently.

Quick Example

You: "What were our top 10 customers last month?"
Claude: *queries your BigQuery database and gives you the answer in plain English*

No more writing SQL queries by hand - just chat naturally with your data!

How Does It Work? 🛠️

This server uses the Model Context Protocol (MCP), which is like a universal translator for AI-database communication. MCP is supported by Claude Desktop, Claude Code, and a growing number of other AI clients.

Here’s all you need to do:

1. Set up authentication (see below)
2. Add your project details to your MCP client’s config file
3. Start chatting with your BigQuery data naturally!

What Can It Do? 📊

• Read-only by design — only SELECT statements are allowed. Every query is validated by BigQuery’s own dry-run planner before execution, so INSERT, UPDATE, DELETE, DROP, TRUNCATE, EXPORT DATA, and MERGE are all rejected. The AI agent cannot mutate your warehouse, period.
• Run SQL queries by just asking questions in plain English
• Access both tables and materialized views in your datasets
• Explore dataset schemas with clear labeling of resource types (tables vs views)
• Analyze data within configurable safe limits (set via config.json or --maximum-bytes-billed)
• Protect sensitive data — define field-level access restrictions to prevent AI agents from reading PII, PHI, financial data, and secrets. The agent receives clear guidance on how to reformulate queries using aggregates or EXCEPT clauses, so it remains useful without exposing individual records.
• Auto-discover sensitive fields — automatically scan your entire BigQuery data warehouse for columns matching sensitive patterns (names, emails, SSNs, medical records, API keys, etc.) and add them to the restricted list. New tables and columns are protected automatically on each scan — no manual maintenance required.
• Fully configurable — everything is driven by config.json. Add your own detection patterns to match your organization’s naming conventions (e.g., %guardian_name%, %beneficiary%), adjust scan frequency, set billing limits, and define per-table field restrictions. The scanner picks up your custom patterns on the next run and automatically protects any matching columns across all datasets.

Which Setup Is Right for You?