Baryhuang MCP Server Aws Resources Python

🚀 AWS Resources MCP Server

This is an AWS Resources MCP Server implemented in Python, aiming to query and manage AWS resources through the boto3 library. The server supports multiple AWS services, including S3, EC2, DynamoDB, etc., and can be run in a Docker container.

🚀 Quick Start

Why Create Another AWS MCP Server

We created this project to provide a simpler and more flexible solution for managing and querying AWS resources. Compared with existing tools, our implementation is more lightweight and easier to integrate into various environments.

✨ Features

Supported Services

• S3: List buckets, upload/download files, etc.
• EC2: Query instance status, start/stop instances, etc.
• DynamoDB: Read/write table data, query indexes, etc.
• IAM: Manage users and permissions.

Example Queries

# List all S3 buckets
{
  "operation": "list_buckets",
  "params": {}
}

# Query EC2 instance status
{
  "operation": "describe_instances",
  "params": {
    "InstanceIds": ["i-12345678"]
  }
}

📦 Installation

Docker Installation

Supported Platforms

• Linux/amd64
• Linux/arm64
• Linux/arm/v7

Option 1: Pull from Docker Hub

docker pull buryhuang/mcp-server-aws-resources:latest

Option 2: Build Locally

docker build -t mcp-server-aws-resources .

Run the container:

docker run \
  -e AWS_ACCESS_KEY_ID=your_access_key_id_here \
  -e AWS_SECRET_ACCESS_KEY=your_secret_access_key_here \
  -e AWS_DEFAULT_REGION=us-east-1 \
  buryhuang/mcp-server-aws-resources:latest

Or use a configuration file and local credentials:

docker run \
  -e AWS_PROFILE=default \
  -v ~/.aws:/root/.aws \
  buryhuang/mcp-server-aws-resources:latest

Cross - Platform Publishing

To push the image to multiple platforms, follow these steps:

1. Create a new builder instance (if not already created):

   docker buildx create --use
   

2. Build and push a multi - platform image:

   docker buildx build --platform linux/amd64,linux/arm64,linux/arm/v7 -t buryhuang/mcp-server-aws-resources:latest --push .
   

3. Verify that the image is available for the specified platform:

   docker buildx imagetools inspect buryhuang/mcp-server-aws-resources:latest
   

💻 Usage Examples

Running with Docker in Claude Desktop

Example 1: Using ACCESS_KEY_ID and SECRET_ACCESS_KEY

{
  "mcpServers": {
    "aws-resources": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "AWS_ACCESS_KEY_ID=your_access_key_id_here",
        "-e",
        "AWS_SECRET_ACCESS_KEY=your_secret_access_key_here",
        "-e",
        "AWS_DEFAULT_REGION=us-east-1",
        "buryhuang/mcp-server-aws-resources:latest"
      ]
    }
  }
}

Example 2: Using PROFILE and Mounting Local AWS Credentials

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

Usage Instructions

1. Configure AWS Credentials: Ensure that AWS credentials (Access Key ID and Secret Access Key) are correctly configured before running the container.
2. Select a Region: You can specify the default region by setting the AWS_DEFAULT_REGION environment variable or explicitly specify the region in the call.
3. Use the API: Send HTTP requests to the MCP server, including the required AWS operations and parameters.

Code Example

import requests

# Initialize the MCP server address
mcp_server_url = "http://localhost:5000"

# List S3 buckets
response = requests.post(mcp_server_url, json={
    "operation": "list_buckets",
    "params": {}
})
print(response.json())

# Query EC2 instance status
response = requests.post(mcp_server_url, json={
    "operation": "describe_instances",
    "params": {
        "InstanceIds": ["i-12345678"]
    }
})
print(response.json())

⚠️ Important Note

• Ensure the security of AWS credentials to avoid leakage.
• Regularly update the dependency libraries to get the latest features and security fixes.

Through the above steps, you can easily deploy and use this MCP server to manage and query AWS resources in a local or cloud environment.