Zoroken123/gcp-mcp-server

GCP MCP Server 🚀

Welcome to the GCP MCP Server repository! This project provides a robust integration of Google Cloud Platform (GCP) services tailored for Generative AI applications. With this server, you can easily harness the power of GCP's capabilities, enabling seamless interaction between AI agents and cloud resources.

Table of Contents

1. Introduction
2. Features
3. Technologies Used
4. Installation
5. Usage
6. Contributing
7. License
8. Releases
9. Contact

Introduction

The GCP MCP Server serves as a central hub for integrating various GCP services, including BigQuery, Vertex AI, and other essential tools. This project aims to simplify the development process for AI applications by providing a unified platform that adheres to the Model Context Protocol (MCP).

Features

• Seamless GCP Integration: Connect effortlessly with GCP services to enhance your AI applications.
• Support for Multiple AI Agents: Deploy and manage various AI agents within the same environment.
• Data Handling with BigQuery: Utilize BigQuery for efficient data storage and querying.
• Robust Infrastructure: Built on a solid foundation to ensure reliability and performance.
• Easy Setup: Get started quickly with clear installation instructions.

Technologies Used

• Google Cloud Platform: The backbone of the server, providing scalable resources.
• BigQuery: For handling large datasets and performing complex queries.
• Vertex AI: To manage and deploy machine learning models effectively.
• Model Context Protocol (MCP): Ensures structured communication between AI agents and the server.
• Node.js: The runtime environment for the server.
• Express.js: For building web applications and APIs.

Installation

To set up the GCP MCP Server, follow these steps:

1. Clone the Repository:

   git clone https://github.com/Zoroken123/gcp-mcp-server.git
   cd gcp-mcp-server
   

2. Install Dependencies:

   Use npm to install the required packages:

   npm install
   

3. Set Up Google Cloud Credentials:

   Ensure you have a Google Cloud account and set up your credentials. You can follow the GCP documentation for guidance.

4. Run the Server:

   Start the server with the following command:

   npm start
   

The server will now be running on your local machine.

Usage

Once the server is up and running, you can interact with it using HTTP requests. The API provides endpoints for managing AI agents, querying data from BigQuery, and integrating with Vertex AI.

Example API Request

To add a new AI agent, send a POST request to the /agents endpoint:

curl -X POST http://localhost:3000/agents -H "Content-Type: application/json" -d '{"name": "Agent1", "type": "chatbot"}'

Documentation

For detailed API documentation, refer to the docs folder in the repository. It contains examples, endpoint descriptions, and usage scenarios.

Contributing

We welcome contributions from the community! If you want to help improve the GCP MCP Server, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your branch and create a pull request.

Please ensure your code adheres to our coding standards and includes appropriate tests.

License

This project is licensed under the MIT License. See the file for details.

Releases

For the latest releases, please visit our Releases section. You can download the latest version and execute it to start using the GCP MCP Server.

Contact

For any questions or support, feel free to reach out:

• Author: Zoroken123
• Email: your-email@example.com

Thank you for checking out the GCP MCP Server! We hope you find it useful for your AI projects. For more information, visit our Releases section to download the latest version.