MCP-AWS

by anirban1592

Integrations

  • Uses .ENV files for configuration and secure storage of AWS credentials and API keys needed for the MCP server operation.

  • Integrates with GitHub repositories for code access and deployment, allowing users to clone the necessary code components for the MCP server implementation.

  • Incorporates Google's A2A (Agent-to-Agent) protocol for enabling robust multi-agent communication between AI systems.

🚀 Multi-Agent System: A2A & MCP Integration POC

POC: Integrating A2A, MCP, and OpenAI Agents for AWS Tasks 🖥️✨


🎥 Demo Video

Watch the demo video to see MCP-AWS in action! 🚀


🌟 Features

  1. 🚀 Seamless Protocol Integration: Demonstrates the successful integration of the Agent-to-Agent (A2A) protocol with a Model Context Protocol (MCP) server for robust multi-agent communication.
  2. 🧠 Leverages OpenAI Agents SDK: Built upon the powerful OpenAI Agents SDK to create intelligent agents capable of understanding and acting on user prompts.
  3. ☁️ Automated Cloud Management: Enables direct provisioning and termination of AWS EC2 instances through simple user interactions, showcasing practical tool execution via the MCP.

🛠️ Tools in the MCP Server

The MCP server is a custom server with two tools:

  1. initiate_aws_ec2_instance: Creates an AWS EC2 instance.
  2. terminate_aws_ec2_instance: Terminates an AWS EC2 instance by its ID.

🚀 Getting Started

Prerequisites

  1. Python 3.12+ (for local setup) or Docker (for containerized setup)
  2. AWS IAM Role: Create an IAM role with the necessary permissions to manage EC2 instances.
  3. Environment Variables: Prepare a .env file with the following variables:
    • AWS_ACCESS_KEY_ID
    • AWS_SECRET_ACCESS_KEY
    • AWS_DEFAULT_REGION
    • OPENAI_API_KEY
    • AMI_ID
    • INSTANCE_TYPE
    • KEY_NAME
    • SECURITY_GROUP_IDS
    • AWS_REGION

🏃‍♂️ Running the App

  1. Clone the repository at the root:
    git clone https://github.com/anirban1592/google_openai_mcp.git cd google_openai_mcp
  2. Create .env file as shown in prerequisites
  3. Run the remote agent example:
    cd openai-agent/ uv run .
  4. Clone the A2A client code(by google) at the root dir:
    git clone https://github.com/google/A2A.git cd demo/ui
  5. Create an environment file with your API key or enter it directly in the UI when prompted:
    echo "GOOGLE_API_KEY=your_api_key_here" >> .env
  6. Run the front end example:
    uv run main.py
  7. Refer to the attached video to see it in action

💬 Using the AI Agent

  1. To create an EC2 instance:
    Enter your command: Create an EC2 instance
  2. To terminate an EC2 instance:
    Enter your command: Terminate EC2 instance with ID <instance-id>

⚠️ Word of Caution

  • IAM Role and Credentials: Please create AWS IAM roles and credentials at your own risk. Ensure you follow AWS best practices for security.
  • Billing and Security: This app is a proof of concept (POC) and is intended for learning purposes only. We are not responsible for any billing issues or security incidents.

📚 Learnings

This project demonstrates:

  1. How to integrate MCP servers with OpenAI Agents SDK
  2. How to build a simple AI-driven application for AWS resource management

Enjoy exploring the power of AI and MCP servers! 🌟

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security - not tested
F
license - not found
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quality - not tested

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A custom server with tools that enable AI agents to provision and terminate AWS EC2 instances through natural language commands.

  1. 🎥 Demo Video
    1. 🌟 Features
      1. 🛠️ Tools in the MCP Server
        1. 🚀 Getting Started
          1. Prerequisites
          2. 🏃‍♂️ Running the App
          3. 💬 Using the AI Agent
        2. ⚠️ Word of Caution
          1. 📚 Learnings

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