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Bigquery MCP Server

@PaddyAlton

リポジトリ

基本情報

カテゴリ

データベース

ライセンス

MIT

トランスポート

stdio

作者

PaddyAlton

Bigquery MCP Server について

An MCP server to help AI Agents inspect the contents of a BigQuery warehouse

#bigquery#mcp#ai-agents

Bigquery MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "bigquery-mcp": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp",
        "--directory",
        "/path/to/bigquery-mcp",
        "mcp",
        "run",
        "/path/to/bigquery-mcp/src/server.py"
      ],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": ""
      }
    }
  }
}

Bigquery MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is bigquery-mcp?

bigquery-mcp is a Model Context Protocol (MCP) server that provides AI agents with tools to examine the contents of a BigQuery data warehouse—including datasets, tables, columns, and query history. It is designed for developers using AI-powered coding tools (e.g., Cursor) to improve the quality of SQL queries by giving the agent context about the database schema and data.

How to use bigquery-mcp?

After ensuring uv is installed and a BigQuery client is available (via gcloud or a service account key set in GOOGLE_APPLICATION_CREDENTIALS), clone the repository and run uv sync. Then, in Cursor settings under MCP Servers, start a server with the command: uv run --with mcp --directory /path/to/bigquery-mcp mcp run /path/to/bigquery-mcp/src/server.py. A contextual rule file (.cursor/rules/tool-use-rule.mdc) is recommended to give the Cursor Agent proper instructions for using the tools.

Key features of bigquery-mcp

  • Provides AI agents with tools to list BigQuery datasets, tables, and columns.
  • Lets agents inspect query history for context.
  • Helps agents write better SQL by exposing database contents.
  • Easy integration with Cursor IDE via a single command.
  • Relies on uv for dependency management and BigQuery client library for authentication.

Use cases of bigquery-mcp

  • An AI coding agent needs to write a query but lacks schema awareness; it uses bigquery-mcp to list all tables and their columns first.
  • Exploring a new BigQuery project: an agent can fetch dataset and table names before generating analysis queries.
  • Reviewing past query patterns by inspecting BigQuery’s query history through the MCP tools.

よくある質問

1

What prerequisites are required to run bigquery-mcp?

You need `uv` installed (e.g., via `brew install uv`) and a BigQuery client that can transparently create a client—usually from having `gcloud` installed locally. Alternatively, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to a service account key.

2

How do I start bigquery-mcp in Cursor?

In Cursor settings, go to MCP Servers and add a new server with the command: `uv run --with mcp --directory /path/to/bigquery-mcp mcp run /path/to/bigquery-mcp/src/server.py`.

3

What tools does bigquery-mcp give to an AI agent?

The server provides tools to examine BigQuery datasets, tables, columns, and query history, giving the agent the context it needs to write better SQL queries.

4

What are the development dependencies?

For development you also need `Taskfile` installed (e.g., `brew install go-task`). The project uses a Taskfile for task automation.

5

Does bigquery-mcp assume any particular authentication method?

It currently assumes you can transparently create a BigQuery `Client`. This works when `gcloud` is installed locally—in other environments you can provide a service account key via the `GOOGLE_APPLICATION_CREDENTIALS` environment variable.

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