BigQuery MCP Server

<div align="center"> <img src="assets/mcp-bigquery-server-logo.png" alt="BigQuery MCP Server Logo" width="400"/> </div>

What is this? 🤔

This is a server that lets your LLMs (like Claude) talk directly to your BigQuery data! 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. While MCP is designed to work with any AI model, right now it's available as a developer preview in Claude Desktop.

Here's all you need to do:

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

What Can It Do? 📊

• 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 safe limits (1GB query limit by default)
• Keep your data secure (read-only access)

Quick Start 🚀

Prerequisites

• Node.js 14 or higher
• Google Cloud project with BigQuery enabled
• Either Google Cloud CLI installed or a service account key file
• Claude Desktop (currently the only supported LLM interface)

Option 1: Quick Install via Smithery (Recommended)

To install BigQuery MCP Server for Claude Desktop automatically via Smithery, run this command in your terminal:

npx @smithery/cli install @ergut/mcp-bigquery-server --client claude

The installer will prompt you for:

• Your Google Cloud project ID
• BigQuery location (defaults to us-central1)

Once configured, Smithery will automatically update your Claude Desktop configuration and restart the application.

Option 2: Manual Setup

If you prefer manual configuration or need more control:

1. Authenticate with Google Cloud (choose one method):

   • Using Google Cloud CLI (great for development):

     gcloud auth application-default login
     

   • Using a service account (recommended for production):

     # Save your service account key file and use --key-file parameter
     # Remember to keep your service account key file secure and never commit it to version control
     

2. Add to your Claude Desktop config Add this to your claude_desktop_config.json:

   • Basic configuration:

     {
       "mcpServers": {
         "bigquery": {
           "command": "npx",
           "args": [
             "-y",
             "@ergut/mcp-bigquery-server",
             "--project-id",
             "your-project-id",
             "--location",
             "us-central1"
           ]
         }
       }
     }
     

   • With service account:

     {
       "mcpServers": {
         "bigquery": {
           "command": "npx",
           "args": [
             "-y",
             "@ergut/mcp-bigquery-server",
             "--project-id",
             "your-project-id",
             "--location",
             "us-central1",
             "--key-file",
             "/path/to/service-account-key.json"
           ]
         }
       }
     }
     

3. Start chatting! Open Claude Desktop and start asking questions about your data.

Command Line Arguments

The server accepts the following arguments:

• --project-id: (Required) Your Google Cloud project ID
• --location: (Optional) BigQuery location, defaults to 'us-central1'
• --key-file: (Optional) Path to service account key JSON file

Example using service account:

npx @ergut/mcp-bigquery-server --project-id your-project-id --location europe-west1 --key-file /path/to/key.json

Permissions Needed

You'll need one of these:

• roles/bigquery.user (recommended)
• OR both:
  • roles/bigquery.dataViewer
  • roles/bigquery.jobUser

Developer Setup (Optional) 🔧

Want to customize or contribute? Here's how to set it up locally:

# Clone and install
git clone https://github.com/ergut/mcp-bigquery-server
cd mcp-bigquery-server
npm install

# Build
npm run build

Then update your Claude Desktop config to point to your local build:

{
  "mcpServers": {
    "bigquery": {
      "command": "node",
      "args": [
        "/path/to/your/clone/mcp-bigquery-server/dist/index.js",
        "--project-id",
        "your-project-id",
        "--location",
        "us-central1",
        "--key-file",
        "/path/to/service-account-key.json"
      ]
    }
  }
}

Current Limitations ⚠️

• MCP support is currently only available in Claude Desktop (developer preview)
• Connections are limited to local MCP servers running on the same machine
• Queries are read-only with a 1GB processing limit
• While both tables and views are supported, some complex view types might have limitations

Support & Resources 💬

• 🐛 Report issues
• 💡 Feature requests
• 📖 Documentation

License 📝

MIT License - See LICENSE file for details.

Author ✍️

Salih Ergüt

Sponsorship

This project is proudly sponsored by:

<div align="center"> <a href="https://www.oredata.com"> <img src="assets/oredata-logo-nobg.png" alt="OREDATA" width="300"/> </a> </div>

Version History 📋

See CHANGELOG.md for updates and version history.