AWS MCP Server

A suite of specialized MCP servers that help you get the most out of AWS, wherever you use MCP.

[!TIP] The Agent Toolkit for AWS is now live! The Agent Toolkit for AWS is the successor to the MCP servers, plugins, and skills available on AWS Labs, and was informed by feedback from customers like you. If you're building production software using coding agents or building agents for your own customers, we recommend Agent Toolkit for AWS. It includes IAM condition keys to distinguish agent actions from human ones, CloudWatch and CloudTrail visibility, and skills that have been evaluated for accuracy and effectiveness. This repo continues to work and accept contributions. Over time, the most useful projects here will move into Agent Toolkit for AWS.

Table of Contents

• Open source MCP servers for AWS
  • Table of Contents
  • What is the Model Context Protocol (MCP) and how does it work with MCP Servers for AWS?
  • Open source MCP servers for AWS Transport Mechanisms
    • Supported transport mechanisms
    • Server Sent Events Support Removal
    • Why MCP Servers for AWS?
  • Available MCP Servers: Quick Installation
    • 🚀 Getting Started with AWS
    • Browse by What You're Building
      • 📚 Real-time access to official AWS documentation
    • 🏗️ Infrastructure & Deployment
      • Container Platforms
      • Serverless & Functions
      • Support
    • 🤖 AI & Machine Learning
    • 📊 Data & Analytics
      • SQL & NoSQL Databases
        • Search & Analytics
      • Backend API Providers
      • Caching & Performance
    • 🛠️ Developer Tools & Support
    • 📡 Integration & Messaging
    • 💰 Cost & Operations
    • 🧬 Healthcare & Lifesciences
    • Browse by How You're Working
      • 👨‍💻 Vibe Coding & Development
        • Core Development Workflow
        • Infrastructure as Code
        • Application Development
        • Container & Serverless Development
        • Testing & Data
        • Lifesciences Workflow Development
        • Healthcare Data Management
      • 💬 Conversational Assistants
        • Knowledge & Search
        • Content Processing & Generation
        • Business Services
      • 🤖 Autonomous Background Agents
        • Data Operations & ETL
        • Caching & Performance
        • Workflow & Integration
        • Operations & Monitoring
  • MCP AWS Lambda Handler Module
  • When to use Local vs Remote MCP Servers?
    • Local MCP Servers
    • Remote MCP Servers
  • Use Cases for the Servers
  • Installation and Setup
    • For macOS/Linux
    • For Windows
    • Running MCP servers in containers
    • Getting Started with Kiro
      • ~/.kiro/settings/mcp.json
    • Getting Started with Cline and Amazon Bedrock
      • cline_mcp_settings.json
    • Getting Started with Cursor
      • .cursor/mcp.json
    • Getting Started with Windsurf
      • ~/.codeium/windsurf/mcp_config.json
    • Getting Started with VS Code
      • .vscode/mcp.json
    • Getting Started with Claude Code
      • .mcp.json
  • Samples
  • Vibe coding
  • Additional Resources
  • Security
  • Contributing
  • Developer guide
  • License
  • Disclaimer

What is the Model Context Protocol (MCP) and how does it work with MCP Servers for AWS?

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.

— Model Context Protocol README

An MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. Host applications (such as chatbots, IDEs, and other AI tools) have MCP clients that maintain 1:1 connections with MCP servers. Common MCP clients include agentic AI coding assistants (like Kiro, Cline, Cursor, Windsurf) as well as chatbot applications like Claude Desktop, with more clients coming soon. MCP servers can access local data sources and remote services to provide additional context that improves the generated outputs from the models.

MCP Servers for AWS use this protocol to provide AI applications access to AWS documentation, contextual guidance, and best practices. Through the standardized MCP client-server architecture, AWS capabilities become an intelligent extension of your development environment or AI application.

MCP Servers for AWS enable enhanced cloud-native development, infrastructure management, and development workflows—making AI-assisted cloud computing more accessible and efficient.

The Model Context Protocol is an open source project run by Anthropic, PBC. and open to contributions from the entire community. For more information on MCP, you can find further documentation here

Open source MCP servers for AWS Transport Mechanisms

Supported transport mechanisms

The MCP protocol currently defines two standard transport mechanisms for client-server communication:

• stdio, communication over standard in and standard out
• streamable HTTP

The MCP servers in this repository are designed to support stdio only.

You are responsible for ensuring that your use of these servers comply with the terms governing them, and any laws, rules, regulations, policies, or standards that apply to you.

Server Sent Events Support Removal

Important Notice: On May 26th, 2025, Server Sent Events (SSE) support was removed from all MCP servers in their latest major versions. This change aligns with the Model Context Protocol specification's backwards compatibility guidelines.

We are actively working towards supporting Streamable HTTP, which will provide improved transport capabilities for future versions.

For applications still requiring SSE support, please use the previous major version of the respective MCP server until you can migrate to alternative transport methods.

Why MCP Servers for AWS?

MCP servers enhance the capabilities of foundation models (FMs) in several key ways:

• Improved Output Quality: By providing relevant information directly in the model's context, MCP servers significantly improve model responses for specialized domains like AWS services. This approach reduces hallucinations, provides more accurate technical details, enables more precise code generation, and ensures recommendations align with current AWS best practices and service capabilities.

• Access to Latest Documentation: FMs may not have knowledge of recent releases, APIs, or SDKs. MCP servers bridge this gap by pulling in up-to-date documentation, ensuring your AI assistant always works with the latest AWS capabilities.

• Workflow Automation: MCP servers convert common workflows into tools that foundation models can use directly. Whether it's CDK, Terraform, or other AWS-specific workflows, these tools enable AI assistants to perform complex tasks with greater accuracy and efficiency.

• Specialized Domain Knowledge: MCP servers provide deep, contextual knowledge about AWS services that might not be fully represented in foundation models' training data, enabling more accurate and helpful responses for cloud development tasks.

Available MCP Servers: Quick Installation

Get started quickly with one-click installation buttons for popular MCP clients. Click the buttons below to install servers directly in Cursor or VS Code:

🚀 Getting Started with AWS

For AWS interactions, we recommend starting with:

Browse by What You're Building

📚 Real-time access to official AWS documentation

🏗️ Infrastructure & Deployment

Build, deploy, and manage cloud infrastructure with Infrastructure as Code best practices.

Container Platforms

Serverless & Functions

Migration & Modernization

Support

🤖 AI & Machine Learning

Enhance AI applications with knowledge retrieval, content generation, and ML capabilities

Documentation truncated — see the full README on GitHub.