# Google BigQuery — how to use (mcp.ai)

Google BigQuery is a fully managed data warehouse for large-scale data analytics, offering fast SQL queries and machine learning capabilities on massive datasets

## Option A — via MCP (recommended)
Remote MCP endpoint (HTTP, streamable): `https://api.mcp.ai/p_googlebigquery?ms=1781541840000`
Add it as a custom/remote MCP connector in your client (Claude, Cursor, VS Code…), then authenticate when prompted. Once connected, ask the agent to use the server's tools (e.g. `googlebigquery_query`).

## Option B — via direct REST API
Base URL: `https://api.mcp.ai/api/googlebigquery`
Auth: `Authorization: Bearer sk_live_…` — create a workspace API key at https://mcp.ai/settings/api-keys
Discover endpoints: `GET https://api.mcp.ai/api/googlebigquery/_endpoints`

### Endpoints
- `POST https://api.mcp.ai/api/googlebigquery/query` — Query tool will run a sql query in bigquery. note: make sure the query being input in a single line format. for example, select * from sample dataset.sample table where column name = 'value'
  - body: { query: string }

## More
- Page: https://mcp.ai/googlebigquery
- Agent spec (llms.txt): https://mcp.ai/googlebigquery/llms.txt
- Postman collection: https://mcp.ai/googlebigquery/postman.json