AWS: AI-Driven Automation & Smart Resource Optimization

What is AWS: AI-Driven Automation & Smart Resource Optimization?

AWS: AI-Driven Automation & Smart Resource Optimization is a specialized implementation of the Model Context Protocol (MCP) tailored for AWS operations. This solution automates routine tasks across S3 and DynamoDB services while maintaining granular audit trails. By leveraging AI-driven workflows, it streamlines resource management, reduces manual intervention, and ensures compliance through centralized logging accessible via the audit://aws-operations endpoint.

How to Use AWS: AI-Driven Automation & Smart Resource Optimization?

To deploy the system locally, follow these steps:

1. Clone the repository and configure AWS credentials via environment variables or the AWS CLI.
2. Modify the claude_desktop_config.json file to integrate the server with the Claude desktop app.
3. Execute the server using the specified command structure and validate functionality by performing test operations (e.g., creating an S3 bucket).
4. Refer to MCP’s debugging tools for troubleshooting if unexpected issues arise.

Key Features of AWS: AI-Driven Automation & Smart Resource Optimization

• Service-Specific Automation: Full CRUD support for S3 buckets and DynamoDB tables with advanced batch operations.
• Intelligent Logging: Auto-generated audit logs for all actions, traceable through a dedicated resource endpoint.
• Context-Aware Workflows: Integrates seamlessly with AI models via MCP, enabling dynamic resource provisioning and analysis.
• Batch Execution: Execute PartiQL statements and bulk operations across DynamoDB to optimize throughput and reduce latency.

Use Cases of AWS: AI-Driven Automation & Smart Resource Optimization

Common applications include:

• Automating S3 bucket lifecycle management with TTL configurations.
• Provisioning and scaling DynamoDB tables based on real-time demand patterns.
• Developing AI-driven development environments with pre-configured resource templates.
• Centralizing audit trails for compliance audits and operational reviews.

FAQ from AWS: AI-Driven Automation & Smart Resource Optimization

• Q: How are credentials secured during local execution?
  A: Credentials are managed via environment variables or AWS CLI configurations, adhering to least-privilege IAM principles.
• Q: Can the server handle high-volume DynamoDB operations?
  A: Yes, batch operations and PartiQL execution are optimized for parallel processing and large-scale data manipulation.
• Q: Where can I view the audit logs?
  A: Logs are accessible programmatically via the audit endpoint or through MCP’s integrated tools.
• Q: Does this support multi-region deployments?
  A: The default region is us-east-1, but can be customized via environment variables for cross-region workflows.

AWS MCP Server

A Model Context Protocol server implementation for AWS operations that currently supports S3 and DynamoDB services. All operations are automatically logged and can be accessed through the audit://aws-operations resource endpoint.

See a demo video here.

Listed as a Community Server within the MCP servers repository.

Running locally with the Claude desktop app

1. Clone this repository.
2. Set up your AWS credentials via one of the two methods below. Note that this server requires an IAM user with RW permissions for your AWS account for S3 and DynamoDB.

• Environment variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION (defaults to us-east-1)
• Default AWS credential chain (set up via AWS CLI with aws configure)

3. Add the following to your claude_desktop_config.json file:

• On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
• On Windows: %APPDATA%/Claude/claude_desktop_config.json

"mcpServers": { "mcp-server-aws": { "command": "uv", "args": [ "--directory", "/path/to/repo/mcp-server-aws", "run", "mcp-server-aws" ] } } 4. Install and open the Claude desktop app. 5. Try asking Claude to do a read/write operation of some sort to confirm the setup (e.g. create an S3 bucket and give it a random name). If there are issues, use the Debugging tools provided in the MCP documentation here.

Available Tools

S3 Operations

• s3_bucket_create: Create a new S3 bucket
• s3_bucket_list: List all S3 buckets
• s3_bucket_delete: Delete an S3 bucket
• s3_object_upload: Upload an object to S3
• s3_object_delete: Delete an object from S3
• s3_object_list: List objects in an S3 bucket
• s3_object_read: Read an object's content from S3

DynamoDB Operations

Table Operations

• dynamodb_table_create: Create a new DynamoDB table
• dynamodb_table_describe: Get details about a DynamoDB table
• dynamodb_table_delete: Delete a DynamoDB table
• dynamodb_table_update: Update a DynamoDB table

Item Operations

• dynamodb_item_put: Put an item into a DynamoDB table
• dynamodb_item_get: Get an item from a DynamoDB table
• dynamodb_item_update: Update an item in a DynamoDB table
• dynamodb_item_delete: Delete an item from a DynamoDB table
• dynamodb_item_query: Query items in a DynamoDB table
• dynamodb_item_scan: Scan items in a DynamoDB table

Batch Operations

• dynamodb_batch_get: Batch get multiple items from DynamoDB tables
• dynamodb_item_batch_write: Batch write operations (put/delete) for DynamoDB items
• dynamodb_batch_execute: Execute multiple PartiQL statements in a batch

TTL Operations

• dynamodb_describe_ttl: Get the TTL settings for a table
• dynamodb_update_ttl: Update the TTL settings for a table