MCP Server for AWS Resources

by paihari

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Cloud Service

About

# AWS MCP Server - SyntropAI Ecosystem **Part of the [SyntropAI MCP Ecosystem](https://github.com/paihari/documentation-syntropai)** - A unified multi-cloud abstraction framework. This MCP (Model Context Protocol) server provides secure, dynamic access to AWS services through the innovative SyntropAI abstraction…

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- Universal AWS Access: Dynamic access to all AWS services without hardcoded limitations
- Secure Code Execution: AST-based validation and sandboxed execution environment
- Provider-Agnostic Design: Built on SyntropAI's unified abstraction pattern
- Future-Proof Architecture: Automatically supports new AWS services without updates
- Docker Containerization: Production-ready deployment

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name MCP Server for AWS Resources

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

From the repository

- Python 3.10 or higher
- AWS credentials configured (via ~/.aws/credentials, environment variables, or IAM roles)
- Docker (recommended)
- SyntropAIBox core library

docker run -i --rm \
-e AWS_ACCESS_KEY_ID=your_key \
-e AWS_SECRET_ACCESS_KEY=your_secret \
-e AWS_DEFAULT_REGION=us-east-1 \
mcp-server-aws-resources:latest
```

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "mcp server for aws resources": {
            "mcp-server-for-aws": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "mcp-server-aws-resources",
                    "."
                ]
            }
        }
    }
}

McpServers

{
    "mcp-server-for-aws": {
        "command": "docker",
        "args": [
            "build",
            "-t",
            "mcp-server-aws-resources",
            "."
        ]
    }
}

AWS MCP Server - SyntropAI Ecosystem

Part of the SyntropAI MCP Ecosystem - A unified multi-cloud abstraction framework.

This MCP (Model Context Protocol) server provides secure, dynamic access to AWS services through the innovative SyntropAI abstraction layer. Unlike traditional hardcoded service catalogs, this server supports any AWS service through dynamic SDK access with built-in security sandboxing.

🚀 Key Features

🏗️ Architecture

This server implements the SyntropAI abstraction pattern:

Claude Desktop → MCP Protocol → AWS MCP Server → SyntropAIBox → boto3 → AWS Services

Core Components:

- AWSSession: Unified AWS credential management using BaseSession - AWSResourceQuerier: Secure query execution extending BaseQuerier - AST Sandbox: Safe code execution with timeout protection - Dynamic Schema: Runtime API documentation generation

📋 Prerequisites

- Python 3.10 or higher
- AWS credentials configured (via ~/.aws/credentials, environment variables, or IAM roles)
- Docker (recommended)
- SyntropAIBox core library

🐳 Docker Installation (Recommended)

Build and Run

# Build the image
docker build -t mcp-server-aws-resources .
Run with AWS profile
docker run -i --rm \
  -e AWS_PROFILE=default \
  -v ~/.aws:/root/.aws \
  mcp-server-aws-resources:latest
Run with environment variables
docker run -i --rm \
  -e AWS_ACCESS_KEY_ID=your_key \
  -e AWS_SECRET_ACCESS_KEY=your_secret \
  -e AWS_DEFAULT_REGION=us-east-1 \
  mcp-server-aws-resources:latest

⚙️ Claude Desktop Integration

Add to your claude_config.json:

{
  "mcpServers": {
    "aws-resources": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "AWS_PROFILE=default", 
        "-v", "/Users/yourusername/.aws:/root/.aws",
        "mcp-server-aws-resources:latest"
      ]
    }
  }
}

🛡️ Security Features

AST-Based Validation

- Prevents malicious code injection - Whitelisted imports and functions - Controlled execution environment

Safe Execution

- Timeout protection (2-second default) - Isolated namespace - JSON-serialized responses

Example Safe Query

# User provides this code snippet:
import boto3
ec2 = session.client('ec2')
result = ec2.describe_instances()

The system:
1. ✅ Validates AST syntax
2. ✅ Checks allowed imports (boto3 approved)
3. ✅ Executes in sandbox with timeout
4. ✅ Returns JSON-serialized results

🔧 Usage Examples

List EC2 Instances

import boto3
ec2 = session.client('ec2')
result = ec2.describe_instances()

Create S3 Bucket

python  
import boto3
s3 = session.client('s3')
result = s3.create_bucket(Bucket='my-unique-bucket-name')

Lambda Functions
python
import boto3
lambda_client = session.client('lambda')
result = lambda_client.list_functions()

🌟 SyntropAI Ecosystem Benefits

Unified Multi-Cloud

- Same patterns work across AWS, Azure, OCI - Consistent authentication and error handling - Provider-agnostic abstractions

Non-Hardcoded Services

- Supports any AWS service automatically - No service catalog limitations - Future services work immediately

Enterprise Ready

- Security-first design - Docker containerization - Comprehensive logging

🔗 Related Projects

- Main Documentation: Complete ecosystem overview and architecture
- SyntropAIBox Core: Shared abstraction library
- Azure MCP Server: Azure implementation
- OCI MCP Server: Oracle Cloud implementation
- Finviz MCP Server: Financial data server

🏆 Technical Highlights

This implementation showcases:
- Advanced Abstraction Patterns: Clean separation of concerns
- Security Engineering: AST validation and sandboxed execution
- Cloud Architecture: Scalable, maintainable multi-cloud design
- DevOps Excellence: Containerized, configurable deployment

📞 Support

For questions about the SyntropAI MCP ecosystem:
- Documentation: SyntropAI Documentation Project
- Author: Hari Bantwal ([email protected])

---

This server demonstrates cutting-edge cloud abstraction technology, providing secure, unified access to AWS services through innovative architectural patterns.

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About

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Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

AWS MCP Server - SyntropAI Ecosystem

🚀 Key Features

🏗️ Architecture

Core Components:

📋 Prerequisites

🐳 Docker Installation (Recommended)

Build and Run

Run with AWS profile

Run with environment variables

⚙️ Claude Desktop Integration

🛡️ Security Features

AST-Based Validation

Safe Execution

Example Safe Query

🔧 Usage Examples

List EC2 Instances

Create S3 Bucket

Lambda Functions

🌟 SyntropAI Ecosystem Benefits

Unified Multi-Cloud

Non-Hardcoded Services

Enterprise Ready

🔗 Related Projects

🏆 Technical Highlights

📞 Support

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