awslabs/mcp

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
- Getting Started with fx
- ~/.fx/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.
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> — 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