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AWS MCP Servers

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A suite of MCP servers providing AI applications with access to AWS documentation, contextual guidance, and best practices.

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AWS MCP Servers

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

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Table of Contents

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

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 Q Developer, 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.

AWS MCP Servers 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.

AWS MCP servers 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

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 AWS MCP Servers?

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

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.

Infrastructure as Code
Container Platforms
Serverless & Functions
Support

🤖 AI & Machine Learning

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

📊 Data & Analytics

Work with databases, caching systems, and data processing workflows.

SQL & NoSQL Databases
Search & Analytics
Caching & Performance

🛠️ Developer Tools & Support

Accelerate development with code analysis, documentation, and testing utilities.

📡 Integration & Messaging

Connect systems with messaging, workflows, and location services.

💰 Cost & Operations

Monitor, optimize, and manage your AWS infrastructure and costs.

Browse by How You're Working

👨‍💻 Vibe Coding & Development

AI coding assistants like Amazon Q Developer CLI, Cline, Cursor, and Claude Code helping you build faster

Core Development Workflow
Infrastructure as Code
Application Development
Container & Serverless Development
Testing & Data

💬 Conversational Assistants

Customer-facing chatbots, business agents, and interactive Q&A systems

Knowledge & Search
Content Processing & Generation
Business Services

🤖 Autonomous Background Agents

Headless automation, ETL pipelines, and operational systems

Data Operations & ETL
Caching & Performance
Workflow & Integration
Operations & Monitoring

MCP AWS Lambda Handler Module

A Python library for creating serverless HTTP handlers for the Model Context Protocol (MCP) using AWS Lambda. This module provides a flexible framework for building MCP HTTP endpoints with pluggable session management, including built-in DynamoDB support.

Features:

  • Easy serverless MCP HTTP handler creation using AWS Lambda
  • Pluggable session management system
  • Built-in DynamoDB session backend support
  • Customizable authentication and authorization
  • Example implementations and tests

See src/mcp-lambda-handler/README.md for full usage, installation, and development instructions.

Use Cases for the Servers

For example, you can use the AWS Documentation MCP Server to help your AI assistant research and generate up-to-date code for any AWS service, like Amazon Bedrock Inline agents. Alternatively, you could use the CDK MCP Server or the Terraform MCP Server to have your AI assistant create infrastructure-as-code implementations that use the latest APIs and follow AWS best practices. With the Cost Analysis MCP Server, you could ask "What would be the estimated monthly cost for this CDK project before I deploy it?" or "Can you help me understand the potential AWS service expenses for this infrastructure design?" and receive detailed cost estimations and budget planning insights. The Valkey MCP Server enables natural language interaction with Valkey data stores, allowing AI assistants to efficiently manage data operations through a simple conversational interface.

Samples

Ready-to-use examples of AWS MCP Servers in action are available in the samples directory. These samples provide working code and step-by-step guides to help you get started with each MCP server.

Vibe coding

You can use these MCP servers with your AI coding assistant to vibe code. For tips and tricks on how to improve your vibe coding experience, please refer to our guide.

Additional Resources

Security

See CONTRIBUTING for more information.

Contributing

Big shout out to our awesome contributors! Thank you for making this project better!

contributors

Contributions of all kinds are welcome! Check out our contributor guide for more information.

Developer guide

If you want to add a new MCP Server to the library, check out our development guide and be sure to follow our design guidelines.

License

This project is licensed under the Apache-2.0 License.

Disclaimer

Before using an MCP Server, you should consider conducting your own independent assessment to ensure that your use would comply with your own specific security and quality control practices and standards, as well as the laws, rules, and regulations that govern you and your content.