AWS Trusted Advisor MCP Server: Facilitating Seamless Integration of AWS Tools with AI Applications

Overview: What is AWS Trusted Advisor MCP Server?

The AWS Trusted Advisor MCP (Model Context Protocol) Server provides a robust framework for integrating AWS Trusted Advisor recommendations into AI applications, ensuring secure and efficient workflows. This platform automates the process of identifying low-utilization instances, proposing backup strategies via EBS snapshots, suggesting access key management adjustments, and recommending S3 bucket versioning improvements. The key benefit lies in its ability to leverage AWS resources through a standardized protocol, making it compatible with various AI applications like Claude Desktop, Continue, Cursor, etc.

🔧 Core Features & MCP Capabilities

The AWS Trusted Advisor MCP Server is equipped with several core features that significantly enhance the integration of AWS services into AI workflows:

1. Low-Utilization EC2 Instances: Detects and proposes stopping instances based on utilization metrics.

2. EBS Snapshots: Recommends creating snapshots for volumes lacking proper backups.

3. Exposed Access Keys: Identifies accessible IAM access keys and recommends deactivation.

4. S3 Bucket Versioning: Identifies S3 buckets without versioning enabled, suggesting improvements to ensure data protection.

These features enable a seamless connection between AWS services and AI applications, ensuring that best practices are followed across multiple tools and resources.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the AWS Trusted Advisor MCP Server is designed around Model Context Protocol (MCP), providing a standardized API for interaction with various AWS tools. This protocol ensures that each MCP client can seamlessly request data, execute commands, or receive recommendations from the server without needing to understand AWS services in detail.

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

🚀 Getting Started with Installation

To install the AWS Trusted Advisor MCP Server, follow these steps:

Prerequisites:

• Node.js version 14 or higher.
• npm or yarn for package management.
• An AWS account and an IAM user with Trusted Advisor access rights (or Docker and Docker Compose if running in a containerized environment).

Installation Steps

# Clone the repository
git clone https://github.com/enomoto11/aws-trusted-advisor-mcp-server.git
cd aws-trusted-advisor-mcp-server

# Install dependencies
npm install

Run the server in development mode:

npm run dev

For running within a Docker container, use:

docker-compose up -d
docker-compose logs -f
docker-compose down

AWS Authentication Setup

This project requires authentication via AWS SDK. You may configure this using one of the following methods:

1. .env File: Create .env at the root directory and add your credentials.

   AWS_ACCESS_KEY_ID=your-access-key-id
   AWS_SECRET_ACCESS_KEY=your-secret-access-key
   AWS_SESSION_TOKEN=(optional)
   AWS_REGION=us-east-1
   

2. AWS CLI Configuration:

   aws configure
   

3. Environment Variables:

   export AWS_ACCESS_KEY_ID=your-access-key-id
   export AWS_SECRET_ACCESS_KEY=your-secret-access-key
   export AWS_SESSION_TOKEN=(optional)
   export AWS_REGION=us-east-1
   

💡 Key Use Cases in AI Workflows

The AWS Trusted Advisor MCP Server can be deployed in various real-world scenarios to optimize AI workflows:

Example Use Case: Low-Utilization EC2 Instance Detection and Management

# Detect low-utilization instances and propose stopping them
curl -X POST http://localhost:3001/execute \ 
  -H "Content-Type: application/json" \
  -d '{
    "toolName": "low_utilization_ec2_instances",
    "parameters": {
      "region": "us-east-1",
      "tagKey": "environment",
      "tagValue": "dev"
    }
  }'

Example Use Case: EBS Snapshot Recommendations

# Suggest creating EBS snapshots for backup purposes
curl -X POST http://localhost:3001/execute \ 
  -H "Content-Type: application/json" \
  -d '{
    "toolName": "ebs_snapshot_recommendations",
    "parameters": {
      "region": "us-east-1"
    }
  }'

🔌 Integration with MCP Clients

The AWS Trusted Advisor MCP Server is compatible with the following AI applications:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

📊 Performance & Compatibility Matrix

The performance and compatibility matrix for the AWS Trusted Advisor MCP Server is designed to ensure seamless integration with various AI clients:

Client Integration Matrix

| Client         | Resources | Tools | Prompts |
|----------------|-----------|-------|---------|
| Claude Desktop | ✅         | ✅     | ✅       | Full Support |
| Continue       | ✅         | ✅     | ✅       | Full Support |
| Cursor         | ❌         | ✅     | ❌       | Tools Only   |

🛠️ Advanced Configuration & Security

For more advanced configurations, consider the following:

1. Adding Custom Tools:

   • Define new tools in src/config.ts.
   • Implement logic for these tools in src/tools.ts.
   • Register implementations using the implementTools object.

2. Security Measures:

   • Use environment variables for sensitive data.
   • Securely manage API keys and access tokens.

❓ Frequently Asked Questions (FAQ)

Q1: How do I install the AWS Trusted Advisor MCP Server?

Follow the installation guide provided in the documentation.

Q2: Which AI clients are currently supported by the server?

The supported clients include Claude Desktop, Continue, and Cursor.

Q3: Can I integrate this server with my custom AI application?

Yes, as long as your application can communicate over Model Context Protocol (MCP).

Q4: What tools does the server support currently?

The current tools supported are low-utilization EC2 instance management, EBS snapshot recommendations, and access key deactivation.

Q5: How do I set up environment variables for AWS credentials?

AWS_ACCESS_KEY_ID=your-access-key-id
AWS_SECRET_ACCESS_KEY=your-secret-access-key

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! To contribute, follow these steps:

1. Fork the repository.
2. Clone the forked repository to your local machine.
3. Install the necessary dependencies by running npm install.
4. Make changes and ensure tests pass after committing.
5. Submit a pull request.

🌐 MCP Ecosystem & Resources

For more information on the broader MCP ecosystem, refer to:

• Official Model Context Protocol documentation: [Link]
• Community forums and user groups: [Link]

By integrating AWS Trusted Advisor with AI applications through this server, developers can leverage comprehensive AWS recommendations while maintaining flexibility and compatibility across various tools.

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This comprehensive guide ensures that developers understand how to effectively integrate the AWS Trusted Advisor MCP Server into their AI workflows, providing detailed insights into its architecture, installation steps, configuration options, and integration capabilities.