What is Mcp Bigquery Server

mcp-bigquery-server is a Model Context Protocol (MCP) server that provides secure, read-only access to BigQuery datasets. It acts as a mediator between Large Language Models (LLMs) and BigQuery, allowing users to query and analyze data using natural language without needing to write SQL queries.

Use cases

Use cases for mcp-bigquery-server include data analysis for business intelligence, generating reports by querying customer data, and facilitating data exploration for non-technical users who prefer natural language over SQL.

How to use

To use mcp-bigquery-server, set up authentication with Google Cloud, configure your project details in Claude Desktop’s config file, and start querying your BigQuery data by asking questions in plain English. You can install it via Smithery or manually configure it.

Key features

Key features include the ability to run SQL queries using natural language, access to both tables and materialized views, exploration of dataset schemas, safe data analysis with a 1GB query limit, and secure read-only access to data.

Where to use

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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.