AWS MCP Servers
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Specialized servers that expose AWS capabilities through the Model Context Protocol, allowing AI assistants and other applications to retrieve up‑to‑date AWS documentation, manage infrastructure, query services, and perform workflow automation directly from their context.
AWS MCP Servers Overview
What is AWS MCP Servers about?
AWS MCP Servers provide a suite of lightweight programs that implement the Model Context Protocol to give AI models real‑time, authoritative access to AWS services, documentation, and tooling. By connecting to these servers, AI‑driven assistants can retrieve accurate information, execute infrastructure commands, and interact with data stores, reducing hallucinations and keeping outputs aligned with the latest AWS best practices.
How to use AWS MCP Servers?
- Install a server – use the provided one‑click install badges for Cursor, VS Code, or run the server manually with
uvxor Docker. - Configure the client – add the server entry to your MCP configuration file (e.g.,
~/.aws/amazonq/mcp.json,.cursor/mcp.json, or VS Code settings) specifying the command, arguments, and any required environment variables such as AWS credentials or region. - Invoke from an AI tool – once registered, the client automatically routes relevant requests to the appropriate MCP server, or you can explicitly ask the assistant to use a specific server (e.g., “Using the Terraform MCP Server, create a VPC”).
Key features of AWS MCP Servers
- Real‑time AWS documentation via the AWS Knowledge MCP Server.
- Infrastructure‑as‑Code management with CDK, Terraform, CloudFormation, and Cloud Control API servers.
- Serverless and container tooling for SAM, ECS, EKS, and local container building (Finch).
- Database access for DynamoDB, Aurora (PostgreSQL/MySQL/DSQL), DocumentDB, Neptune, Keyspaces, Timestream, and more.
- AI/ML integration through Bedrock Knowledge Base retrieval, Kendra, Q Business, and custom Bedrock data automation.
- Cost & operations servers for pricing, Cost Explorer, CloudWatch, CloudTrail, and Managed Prometheus.
- Developer utilities such as IAM management, code‑repo research, diagram generation, and synthetic data creation.
- Messaging & workflow servers for SNS/SQS, Step Functions, MQ, and Location Service.
Use cases of AWS MCP Servers
- AI‑assisted cloud engineering – generate IaC templates, validate API calls, and apply changes safely.
- Real‑time troubleshooting – query CloudWatch metrics, fetch recent logs, or look up recent AWS service releases while debugging.
- Cost estimation – ask an assistant to calculate monthly spend for a proposed architecture using the Pricing MCP Server.
- Data‑driven applications – retrieve records from DynamoDB or run SQL queries on Aurora without writing boilerplate code.
- Documentation generation – automatically produce architecture diagrams or code documentation from existing resources.
- Enterprise search – enrich LLM responses with up‑to‑date knowledge from Bedrock Knowledge Bases or Amazon Kendra.
FAQ from the AWS MCP Servers
Q: Do I need to run all servers locally? A: No. You can run only the servers you need, and many (e.g., AWS Knowledge) are offered as fully managed remote services.
Q: How are credentials handled?
A: Servers inherit standard AWS credential resolution (environment variables, shared config files, or IAM roles). For container deployments, mount the .aws directory or provide temporary credentials via environment variables.
Q: Can I use multiple servers simultaneously? A: Yes. The MCP client will negotiate the appropriate server based on the request type, and you can explicitly target a server in your prompt.
Q: What transport does the client use? A: The current version uses stdio; SSE support was removed in May 2025, with Streamable HTTP planned for future releases.
Q: Is there a cost to use the managed servers? A: Managed servers are billed according to standard AWS pricing for the underlying services (e.g., API calls, data transfer). Review each service’s pricing page for details.
AWS MCP Servers's README
AWS MCP Servers
A suite of specialized MCP servers that help you get the most out of AWS, wherever you use MCP.
Table of Contents
- AWS MCP Servers
- Table of Contents
- What is the Model Context Protocol (MCP) and how does it work with AWS MCP Servers?
- Server Sent Events Support Removal
- Why AWS MCP Servers?
- Available MCP Servers: Quick Installation
- 🚀Getting Started with AWS
- Browse by What You're Building
- Browse by How You're Working
- MCP AWS Lambda Handler Module
- When to use Local vs Remote MCP Servers?
- Use Cases for the Servers
- Installation and Setup
- Samples
- Vibe coding
- Additional Resources
- Security
- Contributing
- Developer guide
- License
- Disclaimer
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.
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: 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 general AWS interactions and comprehensive API support, we recommend starting with:
| Server Name | Description | Install |
|---|---|---|
| AWS API MCP Server | 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. |   |
| AWS Knowledge MCP Server | 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. | |
Browse by What You're Building
📚 Real-time access to official AWS documentation
| Server Name | Description | Install |
|---|---|---|
| AWS Knowledge MCP Server | 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. | |
| AWS Documentation MCP Server | Get latest AWS docs and API references | |
🏗️ Infrastructure & Deployment
Build, deploy, and manage cloud infrastructure with Infrastructure as Code best practices.
| Server Name | Description | Install |
|---|---|---|
| AWS Cloud Control API MCP Server | Direct AWS resource management with security scanning and best practices | |
| AWS CDK MCP Server | AWS CDK development with security compliance and best practices | |
| AWS Terraform MCP Server | Terraform workflows with integrated security scanning | |
| AWS CloudFormation MCP Server | Direct CloudFormation resource management via Cloud Control API | |
Container Platforms
| Server Name | Description | Install |
|---|---|---|
| Amazon EKS MCP Server | Kubernetes cluster management and application deployment | |
| Amazon ECS MCP Server | Container orchestration and ECS application deployment | |
| Finch MCP Server | Local container building with ECR integration | |
Serverless & Functions
| Server Name | Description | Install |
|---|---|---|
| AWS Serverless MCP Server | Complete serverless application lifecycle with SAM CLI | |
| AWS Lambda Tool MCP Server | Execute Lambda functions as AI tools for private resource access | |
Support
| Server Name | Description | Install |
|---|---|---|
| AWS Support MCP Server | Help users create and manage AWS Support cases | |
🤖 AI & Machine Learning
Enhance AI applications with knowledge retrieval, content generation, and ML capabilities
| Server Name | Description | Install |
|---|---|---|
| Amazon Bedrock Knowledge Bases Retrieval MCP Server | Query enterprise knowledge bases with citation support | |
| Amazon Kendra Index MCP Server | Enterprise search and RAG enhancement | |
| Amazon Q Business MCP Server | AI assistant for your ingested content with anonymous access | |
| Amazon Q Index MCP Server | Data accessors to search through enterprise's Q index | |
| Nova Canvas MCP Server | AI image generation using Amazon Nova Canvas | |
| Amazon Rekognition MCP Server (deprecated) | Analyze images using computer vision capabilities | |
| AWS Bedrock Data Automation MCP Server | Analyze documents, images, videos, and audio files | |
| AWS Bedrock Custom Model Import MCP Server | Manage custom models in Bedrock for on-demand inference | |
📊 Data & Analytics
Work with databases, caching systems, and data processing workflows.
SQL & NoSQL Databases
| Server Name | Description | Install |
|---|---|---|
| Amazon DynamoDB MCP Server | Complete DynamoDB operations and table management | |
| Amazon Aurora PostgreSQL MCP Server | PostgreSQL database operations via RDS Data API | |
| Amazon Aurora MySQL MCP Server | MySQL database operations via RDS Data API | |
| Amazon Aurora DSQL MCP Server | Distributed SQL with PostgreSQL compatibility | |
| Amazon DocumentDB MCP Server | MongoDB-compatible document database operations | |
| Amazon Neptune MCP Server | Graph database queries with openCypher and Gremlin | |
| Amazon Keyspaces MCP Server | Apache Cassandra-compatible operations | |
| Amazon Timestream for InfluxDB MCP Server | Time-series database operations and InfluxDB compatibility | |
| Amazon MSK MCP Server | Managed Kafka cluster operations and streaming | |
| AWS S3 Tables MCP Server | Manage S3 Tables for optimized analytics | |
| Amazon Redshift MCP Server | Data warehouse operations and analytics queries | |
| AWS IoT SiteWise MCP Server | Industrial IoT asset management, data ingestion, and analytics | |
Search & Analytics
- Amazon OpenSearch MCP Server - OpenSearch powered search, Analytics, and Observability
Backend API Providers
| Server Name | Description | Install |
|---|---|---|
| AWS AppSync MCP Server | Manage and Interact with application backends powered by AWS AppSync | |
Caching & Performance
| Server Name | Description | Install |
|---|---|---|
| Amazon ElastiCache MCP Server | Complete ElastiCache control plane operations | |
| Amazon ElastiCache / MemoryDB for Valkey MCP Server | Advanced data structures and caching with Valkey | |
| Amazon ElastiCache for Memcached MCP Server | High-speed caching with Memcached protocol | |
🛠️ Developer Tools & Support
Accelerate development with code analysis, documentation, and testing utilities.
| Server Name | Description | Install |
|---|---|---|
| AWS IAM MCP Server | Comprehensive IAM user, role, group, and policy management with security best practices | |
| Git Repo Research MCP Server | Semantic code search and repository analysis | |
| Code Documentation Generator MCP Server | Automated documentation from code analysis | |
| AWS Diagram MCP Server | Generate architecture diagrams and technical illustrations | |
| Frontend MCP Server | React and modern web development guidance | |
| Synthetic Data MCP Server | Generate realistic test data for development and ML | |
| OpenAPI MCP Server | Dynamic API integration through OpenAPI specifications | |
📡 Integration & Messaging
Connect systems with messaging, workflows, and location services.
| Server Name | Description | Install |
|---|---|---|
| Amazon SNS / SQS MCP Server | Event-driven messaging and queue management | |
| Amazon MQ MCP Server | Message broker management for RabbitMQ and ActiveMQ | |
| AWS MSK MCP Server | Managed Kafka cluster operations and streaming | |
| AWS Step Functions Tool MCP Server | Execute complex workflows and business processes | |
| Amazon Location Service MCP Server | Place search, geocoding, and route optimization | |
| OpenAPI MCP Server | Dynamic API integration through OpenAPI specifications | |
💰 Cost & Operations
Monitor, optimize, and manage your AWS infrastructure and costs.
| Server Name | Description | Install |
|---|---|---|
| AWS Pricing MCP Server | AWS service pricing and cost estimates | |
| AWS Cost Explorer MCP Server | Detailed cost analysis and reporting | |
| Amazon CloudWatch MCP Server | Metrics, Alarms, and Logs analysis and operational troubleshooting | |
| Amazon CloudWatch Logs MCP Server (deprecated) | CloudWatch Logs analysis and monitoring | |
| AWS Managed Prometheus MCP Server | Prometheus-compatible operations | |
| AWS Billing and Cost Management MCP Server | Billing and cost management | |
🧬 Healthcare & Lifesciences
Interact with AWS HealthAI services.
| Server Name | Description | Install |
|---|---|---|
| AWS HealthOmics MCP Server | Generate, run, debug and optimize lifescience workflows | |
| AWS HealthLake MCP Server | Create, manage, search, and optimize FHIR healthcare data workflows with comprehensive AWS HealthLake integration, featuring automated resource discovery, advanced search capabilities, patient record management, and seamless import/export operations. | |
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
| Server Name | Description | Install |
|---|---|---|
| AWS API MCP Server | 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. | |
| Core MCP Server | Start here: intelligent planning and MCP server orchestration | |
| AWS Knowledge MCP Server | 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. | |
| AWS Documentation MCP Server | Get latest AWS docs and API references | |
| Git Repo Research MCP Server | Semantic search through codebases and repositories | |
Infrastructure as Code
| Server Name | Description | Install |
|---|---|---|
| AWS CDK MCP Server | CDK development with security best practices and compliance | |
| AWS Terraform MCP Server | Terraform with integrated security scanning and best practices | |
| AWS CloudFormation MCP Server | Direct AWS resource management through Cloud Control API | |
| AWS Cloud Control API MCP Server | Direct AWS resource management with security scanning and best practices | |
Application Development
| Server Name | Description | Install |
|---|---|---|
| Frontend MCP Server | React and modern web development patterns with AWS integration | |
| AWS Diagram MCP Server | Generate architecture diagrams as you design | |
| Code Documentation Generation MCP Server | Auto-generate docs from your codebase | |
| OpenAPI MCP Server | Dynamic API integration through OpenAPI specifications | |
Container & Serverless Development
| Server Name | Description | Install |
|---|---|---|
| Amazon EKS MCP Server | Kubernetes cluster management and app deployment | |
| Amazon ECS MCP Server | Containerize and deploy applications to ECS | |
| Finch MCP Server | Local container building with ECR push | |
| AWS Serverless MCP Server | Full serverless app lifecycle with SAM CLI | |
Testing & Data
| Server Name | Description | Install |
|---|---|---|
| Synthetic Data MCP Server | Generate realistic test data for development and ML | |
Lifesciences Workflow Development
| Server Name | Description | Install |
|---|---|---|
| AWS HealthOmics MCP Server | Generate, run, debug and optimize lifescience workflows | |
Healthcare Data Management
| Server Name | Description | Install |
|---|---|---|
| AWS HealthLake MCP Server | Create, manage, search, and optimize FHIR healthcare data workflows with comprehensive AWS HealthLake integration, featuring automated resource discovery, advanced search capabilities, patient record management, and seamless import/export operations. | |
💬 Conversational Assistants
Customer-facing chatbots, business agents, and interactive Q&A systems
Knowledge & Search
| Server Name | Description | Install |
|---|---|---|
| Amazon Bedrock Knowledge Bases Retrieval MCP Server | Query enterprise knowledge bases with citation support | |
| Amazon Kendra Index MCP Server | Enterprise search and RAG enhancement | |
| Amazon Q Business MCP Server | AI assistant for your ingested content with anonymous access | |
| Amazon Q Index MCP Server | Data accessors to search through enterprise's Q index | |
| AWS Documentation MCP Server | Get latest AWS docs and API references | |
Content Processing & Generation
| Server Name | Description | Install |
|---|---|---|
| Amazon Nova Canvas MCP Server | Generate images from text descriptions and color palettes | |
| Amazon Rekognition MCP Server (deprecated) | Analyze images using computer vision capabilities | |
| Amazon Bedrock Data Automation MCP Server | Analyze uploaded documents, images, and media | |
Business Services
| Server Name | Description | Install |
|---|---|---|
| Amazon Location Service MCP Server | Location search, geocoding, and business hours | |
| AWS Pricing MCP Server | AWS service pricing and cost estimates | |
| AWS Cost Explorer MCP Server | Detailed cost analysis and spend reports | |
🤖 Autonomous Background Agents
Headless automation, ETL pipelines, and operational systems
Data Operations & ETL
| Server Name | Description | Install |
|---|---|---|
| AWS Data Processing MCP Server | Comprehensive data processing tools and real-time pipeline visibility across AWS Glue and Amazon EMR-EC2 | |
| Amazon DynamoDB MCP Server | Complete DynamoDB operations and table management | |
| Amazon Aurora PostgreSQL MCP Server | PostgreSQL database operations via RDS Data API | |
| Amazon Aurora MySQL MCP Server | MySQL database operations via RDS Data API | |
| Amazon Aurora DSQL MCP Server | Distributed SQL with PostgreSQL compatibility | |
| Amazon DocumentDB MCP Server | MongoDB-compatible document database operations | |
| Amazon Neptune MCP Server | Graph database queries with openCypher and Gremlin | |
| Amazon Keyspaces MCP Server | Apache Cassandra-compatible operations | |
| Amazon Timestream for InfluxDB MCP Server | Time-series database operations and InfluxDB compatibility | |
| Amazon MSK MCP Server | Managed Kafka cluster operations and streaming | |
Caching & Performance
| Server Name | Description | Install |
|---|---|---|
| Amazon ElastiCache / MemoryDB for Valkey MCP Server | Advanced data structures and caching with Valkey | |
| Amazon ElastiCache for Memcached MCP Server | High-speed caching with Memcached protocol | |
Workflow & Integration
| Server Name | Description | Install |
|---|---|---|
| AWS Lambda Tool MCP Server | Execute Lambda functions as AI tools for private resource access | |
| AWS Step Functions Tool MCP Server | Execute complex workflows and business processes | |
| Amazon SNS/SQS MCP Server | Event-driven messaging and queue management | |
| Amazon MQ MCP Server | Message broker management for RabbitMQ and ActiveMQ | |
| AWS MSK MCP Server | Managed Kafka cluster operations and streaming | |
Operations & Monitoring
| Server Name | Description | Install |
|---|---|---|
| Amazon CloudWatch MCP Server | Metrics, Alarms, and Logs analysis and operational troubleshooting | |
| Amazon CloudWatch Logs MCP Server (deprecated) | CloudWatch Logs analysis and monitoring | |
| Amazon CloudWatch Application Signals MCP Server | Application monitoring and performance insights | |
| AWS Cost Explorer MCP Server | Detailed cost analysis and reporting | |
| AWS Managed Prometheus MCP Server | Prometheus-compatible operations and monitoring | |
| AWS Well-Architected Security Assessment Tool MCP Server | Assess AWS environments against the Well-Architected Framework Security Pillar | |
| AWS CloudTrail MCP Server | CloudTrail events querying and analysis |  |
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.
When to use Local vs Remote MCP Servers?
AWS 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
Note: Some MCP servers, like 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 Cursor and VSCode. Generally, you can:
- Install
uvfrom Astral - Install Python using
uv python install 3.10 - Configure AWS credentials with access to required services
- Add the server to your MCP client configuration
Example configuration for Amazon Q CLI MCP (~/.aws/amazonq/mcp.json):
For macOS/Linux
{
"mcpServers": {
"awslabs.core-mcp-server": {
"command": "uvx",
"args": [
"awslabs.core-mcp-server@latest"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
See individual server READMEs for specific requirements and configuration options.
For Windows
When configuring MCP servers on Windows, you'll need to use a slightly different configuration format:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"disabled": false,
"timeout": 60,
"type": "stdio",
"command": "uv",
"args": [
"tool",
"run",
"--from",
"awslabs.core-mcp-server@latest",
"awslabs.core-mcp-server.exe"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
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.
Running MCP servers in containers
Docker images for each MCP server are published to the public AWS ECR registry.
This example uses docker with the "awslabs.nova-canvas-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--volumeas needed because the"env": {}are not available within the container.{ "mcpServers": { "awslabs.nova-canvas-mcp-server": { "command": "docker", "args": [ "run", "--rm", "--interactive", "--env", "FASTMCP_LOG_LEVEL=ERROR", "--env", "AWS_REGION=us-east-1", "--env-file", "/full/path/to/.env", "--volume", "/full/path/to/.aws:/app/.aws", "public.ecr.aws/awslabs-mcp/awslabs/nova-canvas-mcp-server:latest" ], "env": {} } } } -
For testing local changes you can build and tag the image. You have to update the MCP configuration to use this tag instead of the ECR image.
cd src/nova-canvas-mcp-server docker build -t awslabs/nova-canvas-mcp-server .
Getting Started with Amazon Q Developer CLI
See Amazon Q Developer CLI documentation for details.
-
Access MCP Settings
- Open the Q Developer panel and open the Chat panel.
- Choose the tools icon to access to MCP configuration.
-
Add MCP Servers
- Choose the plus (+) symbol.
- Select the scope: global or local. If you select global scope, the MCP server configuration is stored in ~/.aws/amazonq/mcp.json and available across all your projects. If you select local scope, the configuration is stored in .amazonq/mcp.json within your current project.
- Fill in values as applicable.
-
Manual Configuration
- You can also manually edit the MCP configuration file located at
~/.aws/amazonq/mcp.jsonglobally or.amazonq/mcp.jsonlocally.
- You can also manually edit the MCP configuration file located at
~/.aws/amazonq/mcp.json
For macOS/Linux:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"command": "uvx",
"args": ["awslabs.core-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
For Windows:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"disabled": false,
"timeout": 60,
"type": "stdio",
"command": "uv",
"args": [
"tool",
"run",
"--from",
"awslabs.core-mcp-server@latest",
"awslabs.core-mcp-server.exe"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
Getting Started with Kiro
See Kiro Model Context Protocol Documentation for details.
- Navigate
Kiro>MCP Servers - Add a new MCP server by clicking the
+ Addbutton. - Paste the configuration given below:
kiro_mcp_settings.json
For macOS/Linux:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"command": "uvx",
"args": ["awslabs.core-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
For Windows:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"disabled": false,
"timeout": 60,
"type": "stdio",
"command": "uv",
"args": [
"tool",
"run",
"--from",
"awslabs.core-mcp-server@latest",
"awslabs.core-mcp-server.exe"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
Getting Started with Cline and Amazon Bedrock
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
uvfrom 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.jsonfile.
- In the
cline_mcp_settings.jsonfile, add your desired MCP servers in themcpServersobject. See the following example that will use some of the current AWS MCP servers that are available in this repository. Ensure you save the file to install the MCP servers.
cline_mcp_settings.json
For macOS/Linux:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"command": "uvx",
"args": ["awslabs.core-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"MCP_SETTINGS_PATH": "path to your mcp settings file"
}
}
}
}
For Windows:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"disabled": false,
"timeout": 60,
"type": "stdio",
"command": "uv",
"args": [
"tool",
"run",
"--from",
"awslabs.core-mcp-server@latest",
"awslabs.core-mcp-server.exe"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"MCP_SETTINGS_PATH": "path to your mcp settings file"
}
}
}
}
- 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 AWS MCP Servers. 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
ClinetoAWS Bedrockand selectAWS Profilefor the authentication type. As a note, theAWS Credentialsoption 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.
- Fill out the configuration based on the existing AWS Profile you wish to use, select the desired AWS Region, and enable cross-region inference.
- Next, scroll down on the settings page until you reach the text box that says Custom Instructions. Paste in the following snippet to ensure the
mcp-coreserver is used as the starting point for every prompt:
For every new project, always look at your MCP servers and use mcp-core as the starting point every time. Also after a task completion include the list of MCP servers used in the operation.
-
Once the custom prompt is pasted in, click Done to return to the chat interface.
-
Now you can begin asking questions and testing out the functionality of your installed AWS MCP Servers. The default option in the chat interface is is
Planwhich will provide the output for you to take manual action on (e.g. providing you a sample configuration that you copy and paste into a file). However, you can optionally toggle this toActwhich will allow Cline to act on your behalf (e.g. searching for content using a web browser, cloning a repository, executing code, etc). You can optionally toggle on the "Auto-approve" section to avoid having to click to approve the suggestions, however we recommend leaving this off during testing, especially if you have the Act toggle selected.
Note: For the best results, please prompt Cline to use the desired AWS MCP Server you wish to use. For example, Using the Terraform MCP Server, do...
Getting Started with Cursor
-
Follow the steps above in the Installation and Setup section to install
uvfrom Astral, install Python, and configure AWS credentials with the required services. -
You can place MCP configuration in two locations, depending on your use case:
A. Project Configuration
- For tools specific to a project, create a .cursor/mcp.json file in your project directory.
- This allows you to define MCP servers that are only available within that specific project.
B. Global Configuration
- For tools that you want to use across all projects, create a ~/.cursor/mcp.json file in your home directory.
- This makes MCP servers available in all your Cursor workspaces.
.cursor/mcp.json
For macOS/Linux:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"command": "uvx",
"args": ["awslabs.core-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
For Windows:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"disabled": false,
"timeout": 60,
"type": "stdio",
"command": "uv",
"args": [
"tool",
"run",
"--from",
"awslabs.core-mcp-server@latest",
"awslabs.core-mcp-server.exe"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
-
Using MCP in Chat The Composer Agent will automatically use any MCP tools that are listed under Available Tools on the MCP settings page if it determines them to be relevant. To prompt tool usage intentionally, please prompt Cursor to use the desired AWS MCP Server you wish to use. For example,
Using the Terraform MCP Server, do... -
Tool Approval By default, when Agent wants to use an MCP tool, it will display a message asking for your approval. You can use the arrow next to the tool name to expand the message and see what arguments the Agent is calling the tool with.
Getting Started with Windsurf
-
Follow the steps above in the Installation and Setup section to install
uvfrom Astral, install Python, and configure AWS credentials with the required services. -
Access MCP Settings
- Navigate to Windsurf - Settings > Advanced Settings or use the Command Palette > Open Windsurf Settings Page
- Look for the "Model Context Protocol (MCP) Servers" section
-
Add MCP Servers
- Click "Add Server" to add a new MCP server
- You can choose from available templates like GitHub, Puppeteer, PostgreSQL, etc.
- Alternatively, click "Add custom server" to configure your own server
-
Manual Configuration
- You can also manually edit the MCP configuration file located at
~/.codeium/windsurf/mcp_config.json
- You can also manually edit the MCP configuration file located at
~/.codeium/windsurf/mcp_config.json
For macOS/Linux:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"command": "uvx",
"args": ["awslabs.core-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"MCP_SETTINGS_PATH": "path to your mcp settings file"
}
}
}
}
For Windows:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"disabled": false,
"timeout": 60,
"type": "stdio",
"command": "uv",
"args": [
"tool",
"run",
"--from",
"awslabs.core-mcp-server@latest",
"awslabs.core-mcp-server.exe"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"MCP_SETTINGS_PATH": "path to your mcp settings file"
}
}
}
}
Getting Started with VS Code
Configure MCP servers in VS Code settings or in .vscode/mcp.json (see VS Code MCP docs for more info.):
.vscode/mcp.json
For macOS/Linux:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"command": "uvx",
"args": ["awslabs.core-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
For Windows:
{
"mcpServers": {
"awslabs.core-mcp-server": {
"disabled": false,
"timeout": 60,
"type": "stdio",
"command": "uv",
"args": [
"tool",
"run",
"--from",
"awslabs.core-mcp-server@latest",
"awslabs.core-mcp-server.exe"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
}
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
- Introducing AWS MCP Servers for code assistants
- Vibe coding with AWS MCP Servers | AWS Show & Tell
- Supercharging AWS database development with AWS MCP servers
- AWS costs estimation using Amazon Q CLI and AWS Pricing MCP Server
- Introducing AWS Serverless MCP Server: AI-powered development for modern applications
- Announcing new Model Context Protocol (MCP) Servers for AWS Serverless and Containers
- Accelerating application development with the Amazon EKS MCP server
- Amazon Neptune announces MCP (Model Context Protocol) Server
- Terraform MCP Server Vibe Coding
- How to Generate AWS Architecture Diagrams Using Amazon Q CLI and MCP
- Harness the power of MCP servers with Amazon Bedrock Agents
- Unlocking the power of Model Context Protocol (MCP) on AWS
- AWS Price List Gets a Natural Language Upgrade: Introducing the AWS Pricing MCP Server
- AWS SheBuilds: AWS Team's Journey from Internal Tools to Open Source AI Infrastructure
Security
See CONTRIBUTING for more information.
Contributing
Big shout out to our awesome contributors! Thank you for making this project better!
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.
AWS MCP Servers Reviews
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Actions
AWS MCP Servers's Information
- Author
- awslabs
- Category
- Cloud Platforms
- Language
- Python
- License
- Apache License 2.0
- Stars
- 6,457
- Updated
- 27 days ago
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