What is BigQuery MCP Server?

BigQuery MCP Server is an intermediary service that allows LLMs like Claude to directly query and interact with BigQuery data through natural language processing, eliminating the need for manual SQL query writing.

How to use BigQuery MCP Server?

1. Set up authentication (Google Cloud CLI or service account)
2. Add project details to Claude Desktop's config file
3. Start chatting with your BigQuery data naturally

Installation Options:

1. Quick Install via Smithery:

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

2. Manual Setup:

• Authenticate with Google Cloud
• Configure Claude Desktop's config file
• Start interacting with the server

Key features of BigQuery MCP Server

• Natural language querying of BigQuery data
• Access to tables and materialized views
• Exploration of dataset schemas
• Secure read-only access with 1GB query limit
• Integration with Claude Desktop (current only supported LLM)

Use cases of BigQuery MCP Server

1. Quickly retrieve business insights by asking natural language questions
2. Automate data exploration for analytics teams
3. Provide secure, controlled access to BigQuery data for LLMs
4. Enable non-technical users to query databases without SQL knowledge
5. Integrate AI-driven analytics into workflows

FAQ from BigQuery MCP Server

• What's the current limitation?

Currently only works with Claude Desktop, has a 1GB query limit, and supports read-only operations.

• What permissions are needed?

Either roles/bigquery.user or both roles/bigquery.dataViewer and roles/bigquery.jobUser.

• Can I use this without Claude Desktop?

No, currently MCP is only supported in Claude Desktop.

• How do I set up authentication?

Either through Google Cloud CLI or by using a service account key file.

• What data types are accessible?

Both tables and materialized views in your BigQuery datasets.

BigQuery MCP Server

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:

Version History 📋

See CHANGELOG.md for updates and version history.