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AWS Common MCP Servers with CDK Deployment

@vAirpower

Repository

About AWS Common MCP Servers with CDK Deployment

Deployable MCP Servers for common AWS services (Location, S3, Aurora PG Data API) using AWS CDK.

#aws#mcp#cdk#cloud-services

Basic information

Category

Cloud & Infrastructure

Runtime

python

Transports

stdio

Publisher

vAirpower

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "aws-common-mcp-servers": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is AWS Common MCP Servers with CDK Deployment?

AWS Common MCP Servers with CDK Deployment provides deployable Model Context Protocol (MCP) servers for common AWS services, along with AWS Cloud Development Kit (CDK) code (Python) to provision the necessary infrastructure for running these servers, typically on ECS Fargate. It offers reusable components that AI developers using Amazon Bedrock or LangChain can easily deploy into their AWS accounts to interact with AWS services through the standardized MCP interface.

How to use AWS Common MCP Servers with CDK Deployment?

Deploy by cloning the repository, installing CDK dependencies (pip install -r requirements.txt), bootstrapping CDK (cdk bootstrap), and running cdk deploy --all. For the Aurora PostgreSQL server, you must configure db_cluster_arn and db_secret_arn in the CDK stack file beforehand. Once deployed, the servers run as ECS Fargate tasks and can be invoked from within your AWS environment (e.g., Lambda functions, Bedrock Agents). Local testing is possible by running the server scripts directly after installing dependencies and setting required environment variables.

Key features of AWS Common MCP Servers with CDK Deployment

  • Includes MCP servers for AWS Location Service, S3, and Aurora PostgreSQL
  • Infrastructure provisioned automatically using CDK on ECS Fargate
  • Written in TypeScript (Location Service) and Python (S3, Aurora)
  • Prerequisites: AWS account, Node.js, Python 3.9+, CDK CLI, Docker
  • Aurora server requires an existing cluster with RDS Data API enabled
  • Deployment builds Docker images, pushes to ECR, and creates ECS services

Use cases of AWS Common MCP Servers with CDK Deployment

  • Enable AI agents to search places and calculate routes via AWS Location Service
  • Allow AI agents to list, get, put, and delete objects in S3 buckets
  • Let AI agents execute SQL queries on an Aurora PostgreSQL cluster via RDS Data API
  • Deploy a consistent MCP server infrastructure for multiple AWS services in one account

FAQ from AWS Common MCP Servers with CDK Deployment

Which MCP servers are included?

The project includes three servers: AWS Location Service (TypeScript), Amazon S3 (Python), and Amazon Aurora PostgreSQL via RDS Data API (Python).

What are the prerequisites for the Aurora PostgreSQL server?

You need an existing Aurora PostgreSQL cluster with the RDS Data API enabled, and an AWS Secrets Manager secret containing the database credentials (username, password). The ARNs of both the cluster and the secret must be configured in the CDK stack file.

How do I deploy the servers?

Clone the repository, install CDK dependencies in a Python virtual environment, bootstrap CDK if needed, then run cdk deploy --all (or specific stack names). Deploy all stacks requires Docker running locally to build container images.

How do I access the servers after deployment?

The servers run on ECS Fargate and are not publicly exposed. Interaction happens from within your AWS environment, typically via invoking the ECS tasks from Lambda or Bedrock Agents using the AWS SDK.

How do I clean up the deployed resources?

Run cdk destroy from the cdk_deployment directory after activating the virtual environment. This removes all infrastructure provisioned by the CDK stacks.

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