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mcp-bigquery-server

MCP.Pizza Chef: ergut

The mcp-bigquery-server is a Model Context Protocol (MCP) server that provides secure, read-only access to Google BigQuery datasets. It acts as a standardized interface allowing Large Language Models (LLMs) like Claude to query and analyze data directly from BigQuery without requiring manual SQL queries. This server ensures safe and efficient communication between AI assistants and BigQuery, enabling natural language interactions with complex datasets. By leveraging MCP, it supports seamless integration with various AI models, facilitating real-time data insights and analytics through conversational AI.

Use This MCP server To

Enable LLMs to query BigQuery datasets securely Provide read-only access to BigQuery data for AI models Translate natural language queries into BigQuery SQL Allow conversational data analysis via LLMs Integrate BigQuery data with AI assistants Automate data retrieval from BigQuery using LLMs

README

BigQuery MCP Server

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BigQuery MCP Server Logo

What is this? πŸ€”

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? πŸ› οΈ

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? πŸ“Š

  • 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 πŸš€

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) πŸ”§

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 πŸ’¬

License πŸ“

MIT License - See LICENSE file for details.

Author ✍️

Salih ErgΓΌt

Sponsorship

This project is proudly sponsored by:

Version History πŸ“‹

See CHANGELOG.md for updates and version history.

mcp-bigquery-server FAQ

How does mcp-bigquery-server ensure data security?
It provides read-only access to BigQuery datasets, preventing any data modification and ensuring secure querying.
Can I use mcp-bigquery-server with different LLM providers?
Yes, it supports any LLM compatible with MCP, including Claude, OpenAI, and Gemini.
Do I need to write SQL queries to use this server?
No, the server translates natural language queries from LLMs into BigQuery SQL automatically.
How do I set up authentication for mcp-bigquery-server?
You configure it with your Google Cloud credentials to securely access your BigQuery datasets.
Is the server limited to specific BigQuery datasets?
Access is controlled by your Google Cloud permissions, so it can query any dataset your credentials allow.
Can mcp-bigquery-server handle large query results?
Yes, it streams query results efficiently to the LLM for processing and response generation.
Does it support real-time data querying?
It queries live BigQuery data, providing up-to-date responses based on the latest dataset state.
What programming languages can interact with mcp-bigquery-server?
Any language that can communicate over MCP protocol can interact with the server.