A suite of specialized MCP servers that help you get the most out of AWS, wherever you use MCP.
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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.
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.
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:
Start here for secure, auditable AWS interactions! This remote, managed MCP server is hosted by AWS and combines comprehensive AWS API support with access to the latest AWS documentation, API references, What's New posts, and Getting Started information. Features pre-built Agent SOPs that follow AWS best practices, helping agents complete complex multi-step AWS tasks reliably. Built with safety and control in mind: syntactically validated API calls, IAM-based permissions with zero credential exposure, and complete CloudTrail audit logging. Access all AWS services for managing infrastructure, exploring resources, and executing AWS operations with full transparency and traceability. Read more
A remote, fully-managed MCP server hosted by AWS that provides access to the latest AWS docs, API references, What's New Posts, Getting Started information, Builder Center, Blog posts, Architectural references, and Well-Architected guidance.
Complete Infrastructure as Code toolkit with CloudFormation documentation access, CDK best practices guidance, construct examples, security validation, and deployment troubleshooting
Start here for general AWS interactions! Comprehensive AWS API support with command validation, security controls, and access to all AWS services. Perfect for managing infrastructure, exploring resources, and executing AWS operations through natural language.
A remote, fully-managed MCP server hosted by AWS that provides access to the latest AWS docs, API references, What's New Posts, Getting Started information, Builder Center, Blog posts, Architectural references, and Well-Architected guidance.
Complete Infrastructure as Code toolkit with CloudFormation documentation access, CDK best practices guidance, construct examples, security validation, and deployment troubleshooting
Manage, monitor, and operate SAP applications on AWS with health checks, configuration validation, and scheduling
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
MCP servers can be run either locally on your development machine or remotely on the cloud. Here's when to use each approach:
Local MCP Servers
Development & Testing: Perfect for local development, testing, and debugging
Offline Work: Continue working when internet connectivity is limited
Data Privacy: Keep sensitive data and credentials on your local machine
Low Latency: Minimal network overhead for faster response times
Resource Control: Direct control over server resources and configuration
Remote MCP Servers
Team Collaboration: Share consistent server configurations across your team
Resource Intensive Tasks: Offload heavy processing to dedicated cloud resources
Always Available: Access your MCP servers from anywhere, any device
Automatic Updates: Get the latest features and security patches automatically
Scalability: Easily handle varying workloads without local resource constraints
Security: Centralized security controls with IAM-based permissions and zero credential exposure
Governance: Comprehensive audit logging and compliance monitoring for enterprise-grade governance
Note: Some MCP servers, like the official AWS MCP server (in preview) and AWS Knowledge MCP, are provided as fully managed services by AWS. These AWS-managed remote servers require no setup or infrastructure management on your part - just connect and start using them.
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 AWS Pricing 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.
Installation and Setup
Each server has specific installation instructions with one-click installs for Kiro, Cursor, and VSCode. Generally, you can:
If you have problems with MCP configuration or want to check if the appropriate parameters are in place, you can try the following:
## Run MCP server manually with timeout 15s
$ timeout 15s uv tool run <MCP Name> <args> 2>&1 || echo "Command completed or timed out"
## Example (Aurora MySQL MCP Server)
$ timeout 15s uv tool run awslabs.mysql-mcp-server --resource_arn <Your Resource ARN> --secret_arn <Your Secret ARN> ... 2>&1 || echo "Command completed or timed out"
## If the arguments are not set appropriately, you may see the following message:
usage: awslabs.mysql-mcp-server [-h] --resource_arn RESOURCE_ARN --secret_arn SECRET_ARN --database DATABASE
--region REGION --readonly READONLY
awslabs.mysql-mcp-server: error: the following arguments are required: --resource_arn, --secret_arn, --database, --region, --readonly
Note about performance when using uvx"@latest" suffix:
Using the "@latest" suffix checks and downloads the latest MCP server package from pypi every time you start your MCP clients, but it comes with a cost of increased initial load times. If you want to minimize the initial load time, remove "@latest" and manage your uv cache yourself using one of these approaches:
uv cache clean <tool>: where {tool} is the mcp server you want to delete from cache and install again (e.g.: "awslabs.lambda-tool-mcp-server") (remember to remove the '<>').
uvx <tool>@latest: this will refresh the tool with the latest version and add it to the uv cache.
This example uses docker with the "awslabs.aws-documentation-mcp-server and can be repeated for each MCP server
Optionally save sensitive environmental variables in a file:
# contents of a .env file with fictitious AWS temporary credentials
AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE
AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk
Use the docker options: --env, --env-file, and --volume as needed because the "env": {} are not available within the container.
IMPORTANT: Following these instructions may incur costs and are subject to the Amazon Bedrock Pricing. You are responsible for any associated costs. In addition to selecting the desired model in the Cline settings, ensure you have your selected model (e.g. anthropic.claude-3-7-sonnet) also enabled in Amazon Bedrock. For more information on this, see these AWS docs on enabling model access to Amazon Bedrock Foundation Models (FMs).
Follow the steps above in the Installation and Setup section to install uv from Astral, install Python, and configure AWS credentials with the required services.
If using Visual Studio Code, install the Cline VS Code Extension (or equivalent extension for your preferred IDE). Once installed, click the extension to open it. When prompted, select the tier that you wish. In this case, we will be using Amazon Bedrock, so the free tier of Cline is fine as we will be sending requests using the Amazon Bedrock API instead of the Cline API.
Select the MCP Servers button.
Select the Installed tab, then click Configure MCP Servers to open the cline_mcp_settings.json file.
In the cline_mcp_settings.json file, add your desired MCP servers in the mcpServers object. See the following example that will use some of the current MCP servers that are available in this repository. Ensure you save the file to install the MCP servers.
Once installed, you should see a list of your MCP Servers under the MCP Server Installed tab, and they should have a green slider to show that they are enabled. See the following for an example with two of the possible MCP servers for AWS. Click Done when finished. You should now see the Cline chat interface.
By default, Cline will be set as the API provider, which has limits for the free tier. Next, let's update the API provider to be AWS Bedrock, so we can use the LLMs through Bedrock, which would have billing go through your connected AWS account.
Click the settings gear to open up the Cline settings. Then under API Provider, switch this from Cline to AWS Bedrock and select AWS Profile for the authentication type. As a note, the AWS Credentials option works as well, however it uses a static credentials (Access Key ID and Secret Access Key) instead of temporary credentials that are automatically redistributed when the token expires, so the temporary credentials with an AWS Profile is the more secure and recommended method.
At a glance
Category
Coding
Pricing model
Free
Best for
ai-agents, mcp-server, cloud-platforms
What is AWS MCP Servers? AWS MCP Servers is a coding tool listed on Scriptiii AI Tools. Suite of open-source MCP servers from AWS Labs covering AWS APIs, documentation, IaC and services. Pricing: Free. Free to use. Best for: ai-agents, mcp-server, cloud-platforms.
Questions & answers
Everything you need to know about AWS MCP Servers
What is AWS MCP Servers?
AWS MCP Servers is a coding tool. Suite of open-source MCP servers from AWS Labs covering AWS APIs, documentation, IaC and services.
What does AWS MCP Servers cost?
Free. Free to use.
What is AWS MCP Servers best for?
ai-agents, mcp-server, cloud-platforms. It is filed under Coding.
