Ergut MCP Bigquery Server

🚀 BigQuery MCP Server

This is a server that enables your large language models (LLMs), such as Claude, to directly interact with your BigQuery data. With this server, you can easily establish a connection between the model and the database for more efficient data analysis and processing.

🚀 Quick Start

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

# Start the server
node index.js --project-id your-project-id --location us-central1

✨ Features

• Support for Multiple Data Sources: In addition to BigQuery, it also supports other mainstream database systems.
• Efficient Data Processing: Optimized query execution engine to enhance data processing efficiency.
• Scalable Architecture: Designed to support high - concurrency and large - scale data processing requirements.

📦 Installation

1. Install Node.js: Ensure that the latest version of Node.js and npm are installed on your system.
2. Clone the Code Repository: Clone the project repository from GitHub to your local development environment.
3. Build the Project: Run the npm run build command to build the project.
4. Configure Environment Variables: Set the necessary environment variables, such as database connection information.
5. Start the Service: Start the MCP server using the built executable file.

💻 Usage Examples

Basic Usage

const { MCPClient } = require('mcp-bigquery');

async function main() {
    const client = new MCPClient({
        projectId: 'your-project-id',
        location: 'us-central1'
    });

    const result = await client.query('SELECT * FROM your_dataset.your_table LIMIT 10');
    console.log(result);
}

main().catch(console.error);

📚 Documentation

What is it? 🤔

This is a server that allows your large language models (LLMs, e.g., Claude) to directly interact with your BigQuery data. Through this server, you can easily establish a connection between the model and the database for more efficient data analysis and processing.

How to use it? 💻

1. Set up authentication: Ensure that you have configured the necessary authentication mechanisms to securely access BigQuery.
2. Add project configuration: Add your project information to Claude's configuration file, including the required database connection information.
3. Start the server: Run the BigQuery MCP server and let it listen for requests from Claude.

Technical Details 🔍

• Protocol support: Implemented based on the MCP (Model Context Protocol) to ensure efficient communication between the model and the database.
• Query optimization: The built - in query optimizer can automatically adjust SQL statements to improve execution efficiency.
• Scalability: Supports horizontal scaling by adding server nodes to handle more requests.

Frequently Asked Questions 🤔

• Q: How to handle large amounts of data?
  A: We recommend using pagination queries and parallel processing techniques to improve data loading efficiency.
• Q: Which database systems are supported?
  A: Currently, it supports BigQuery, MySQL, PostgreSQL, and SQLite. More databases are under development.

Documentation and Resources 📖

• Official documentation: Access the documentation for detailed usage guides and technical details.
• Community support: Submit issues and suggest features on GitHub. Our active developer community will assist you.

📄 License

This project is licensed under the MIT License. Please refer to the LICENSE file for details.

Author ✍️

Salih Ergüt