BigQuery - OpenClaw Skill — OpenClaw Tutorials

Install Command

npx clawhub@latest install bigquery

Installation Guide

Check Environment

Make sure Node.js 22+ and OpenClaw are installed. Run openclaw --version in your terminal to verify.

Run Installation

Run the install command above in your terminal. ClawHub will automatically download and install BigQuery to the ~/.openclaw/skills/ directory.

Verify Installation

Run openclaw skills list to check your installed skills and confirm BigQuery appears in the list.

Configure (Optional)

Follow the configuration instructions in the description below to add skill settings to ~/.config/openclaw/openclaw.json5.

Manual Installation: Copy the Skill folder to ~/.openclaw/skills/ or the skills/ directory in your project root. Make sure the folder contains a SKILL.md file.

Natural language to SQL queries Dataset and table schema browsing Query job management and optimization

Detailed Description

BigQuery MCP server brings Google BigQuery's PB-scale data analytics capabilities into AI conversations, supporting natural language queries and SQL execution, enabling non-technical users to easily analyze large-scale datasets.

Core Features

SQL Query (execute_query): Execute SQL queries in BigQuery, supporting standard SQL syntax with automatic partitioning and clustering optimization
Natural Language Query: Describe query requirements in natural language, and AI automatically generates optimized BigQuery SQL
Dataset Browsing (list_datasets/list_tables): List datasets and tables under a project, view table schema, partition info, and row counts
Query Estimation: Estimate the data volume and cost a query will scan before execution
Table Management: Create tables, insert data, manage table expiration and partition settings

Configuration

{
  "mcpServers": {
    "bigquery": {
      "command": "npx",
      "args": ["-y", "@google-cloud/bigquery-mcp-server"],
      "env": {
        "GOOGLE_PROJECT_ID": "your-project-id"
        // Requires gcloud auth or Service Account configuration
      }
    }
  }
}

Use Cases

Business analysis: Natural language queries for sales, user behavior, and other business data
Data exploration: Browse dataset structures, understand available data and field meanings
Cost optimization: Estimate query costs, optimize SQL to reduce data scan volume
ETL monitoring: View BigQuery job statuses and execution history

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