AWS Common MCP Servers

The AWS Common MCP Servers collection provides a ready‑to‑deploy, cloud‑native implementation of the Model Context Protocol for several widely used AWS services. By exposing these services through a standardized MCP interface, the server eliminates the friction that typically accompanies direct API integration—developers no longer need to write custom wrappers or manage authentication for each service. Instead, AI assistants such as Claude can interact with AWS Location Service, Amazon S3, and Aurora PostgreSQL (via the RDS Data API) through a single, consistent set of resources, tools, and prompts.

What Problem Does It Solve?

In many AI‑powered applications, data must be retrieved or manipulated across multiple cloud services. Traditional approaches require developers to write bespoke code for each service, maintain separate credentials, and handle network plumbing manually. This effort is error‑prone and slows iteration cycles. The AWS Common MCP Servers package these services behind a uniform protocol, allowing AI assistants to request data or perform actions with minimal context. The server handles authentication, permission checks, and container orchestration automatically, freeing developers to focus on business logic rather than plumbing.

Core Capabilities

• AWS Location Service – Provides tools for place search, detail retrieval, and route calculation. The TypeScript‑based server exposes a concise set of MCP resources that map directly to the Location Service API, enabling location‑aware workflows such as logistics optimization or geospatial analytics.
• Amazon S3 – A Python server that offers CRUD operations on buckets and objects. It supports listing, uploading, downloading, and deleting files with simple tool calls, making it straightforward to embed file handling into conversational agents.
• Aurora PostgreSQL (RDS Data API) – Allows execution of arbitrary SQL against an Aurora cluster without opening a database port. The server translates MCP tool calls into Data API requests, enabling AI assistants to query or update relational data securely and efficiently.

Each server is containerized and deployed via AWS CDK, typically on ECS Fargate. The CDK stacks provision the necessary VPC, IAM roles, and networking so that the services can run in isolation while remaining accessible to AI clients.

Real‑World Use Cases

• ChatOps for DevOps – An AI assistant can read S3 logs, query an Aurora database for incident metrics, and suggest remediation steps—all through MCP calls.
• Geospatial Planning – A logistics chatbot can calculate optimal routes, retrieve place details, and generate delivery schedules by invoking the Location Service server.
• Data‑Driven Decision Making – Business analysts can ask an AI to pull sales data from Aurora, store visualizations in S3, and share results without writing SQL or managing credentials.

Integration with AI Workflows

Developers can embed these MCP servers into existing LangChain, Bedrock, or custom AI pipelines. By adding the server’s URL to an agent’s tool registry, the assistant gains immediate access to AWS resources. The standardized resource definitions mean that the same prompt structure works across services, and permission scopes can be tightly controlled via IAM policies attached to the ECS task role. This tight integration reduces the cognitive load on both developers and end users, allowing conversations to flow naturally while the backend handles complex AWS interactions.

Unique Advantages

• Zero‑Code Service Exposure – No custom SDK wrappers; the servers expose fully functional tools out of the box.
• Secure, Container‑Based Deployment – Runs on ECS Fargate with IAM‑based authentication, eliminating the need to store secrets in code.
• Modular CDK Infrastructure – Each service stack can be deployed independently, enabling incremental adoption.
• Python & TypeScript Blend – Leverages the strengths of both languages: Python for rapid development and TypeScript for type safety in the Location Service server.

By abstracting AWS service complexity behind a common protocol, the AWS Common MCP Servers empower AI developers to build richer, more integrated applications with less overhead and greater security.