Welcome to AWS MCP Servers
Get started with AWS MCP Servers and learn core features.
The AWS MCP Servers are a suite of specialized MCP servers that help you get the most out of AWS, wherever you use MCP.
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
Why AWS MCP Servers?
MCP servers enhance the capabilities of foundation models (FMs) in several key ways:
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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.
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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.
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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.
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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.
Getting Started Essentials
Before diving into specific AWS services, set up these fundamental MCP servers for working with AWS resources:
AWS API MCP
Set up secure programmatic access to AWS services with credential management and authentication handling. Manage infrastructure, explore resources, and execute AWS operations through natural language.
AWS Knowledge MCP
An AWS-managed remote MCP server that provides instant access to up-to-date AWS docs, API references, What's New posts, Getting Started information, Builder Library, blog posts, architectural references, and contextual guidance.
Available AWS MCP Servers
The servers are organized into these main categories:
- 📚 Documentation: Real-time access to official AWS documentation
- 🏗️ Infrastructure & Deployment: Build, deploy, and manage cloud infrastructure
- 🤖 AI & Machine Learning: Enhance AI applications with knowledge retrieval and ML capabilities
- 📊 Data & Analytics: Work with databases, caching systems, and data processing
- 🛠️ Developer Tools & Support: Accelerate development with code analysis and testing utilities
- 📡 Integration & Messaging: Connect systems with messaging, workflows, and location services
- 💰 Cost & Operations: Monitor, optimize, and manage your AWS infrastructure and costs
- 🧬 Healthcare & Lifesciences: Interact with AWS HealthAI services.
AWS Documentation MCP Server
Get latest AWS docs and APIs
AWS Knowledge MCP Server
Get latest AWS docs, code samples, and other official content
AWS API MCP Server
Interact with AWS services and resources through AWS CLI commands.
AWS CDK MCP Server
AWS CDK development with security compliance
AWS Terraform MCP Server
Terraform workflows with integrated security scanning
AWS CloudFormation MCP Server
Direct CloudFormation resource management via Cloud Control API
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
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
AWS Support MCP Server
Help users create and manage AWS Support cases
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 anonymous MCP Server
AI assistant based on knowledgebase with anonymous access
Amazon Q index MCP Server
Data accessors to search through enterprise's Q index
Amazon Nova Canvas MCP Server
AI image generation with text and color guidance
Amazon Rekognition MCP Server
Analyze images using computer vision capabilities
Amazon Bedrock Data Automation MCP Server
Analyze documents, images, videos, and audio files
Amazon DynamoDB MCP Server
Complete DynamoDB operations and table management
Amazon Aurora PostgreSQL MCP Server
PostgreSQL database operations via RDS Data API
AWS S3 Tables MCP Server
Manage, query, and ingest S3-based tables with support for SQL, CSV-to-table conversion, and metadata discovery.
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
InfluxDB-compatible operations
Amazon ElastiCache MCP Server
Complete ElastiCache operations
Amazon ElastiCache / MemoryDB for Valkey MCP Server
Advanced data structures and caching with Valkey
Amazon ElastiCache for Memcached MCP Server
High-speed caching operations
Git Repo Research MCP Server
Semantic code search and repository analysis
Code Documentation Generation 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
Amazon SNS / SQS MCP Server
Event-driven messaging and queue management
Amazon MQ MCP Server
Message broker management for RabbitMQ and ActiveMQ
AWS Step Functions MCP Server
Execute complex workflows and business processes
Amazon Location Service MCP Server
Place search, geocoding, and route optimization
AWS Pricing MCP Server
Pre-deployment cost estimation and optimization
AWS Cost Explorer MCP Server
Detailed cost analysis and reporting
AWS Managed Prometheus MCP Server
Prometheus-compatible operations
Core MCP Server
Intelligent planning and AWS MCP server orchestration
Amazon Data Processing MCP Server
Comprehensive data processing tools and real-time pipeline visibility across AWS Glue and Amazon EMR-EC2
AWS HealthOmics MCP Server
Generate, run, debug and optimize lifescience workflows on AWS HealthOmics
Amazon CloudWatch Application Signals MCP Server
Application monitoring and performance insights
Amazon CloudWatch MCP Server
Metrics, Alarms, and Logs analysis and operational troubleshooting
AWS IAM MCP Server
Comprehensive IAM user, role, group, and policy management with security best practices
AWS MSK MCP Server
Manage, monitor, and optimize Amazon MSK clusters with best practices
Amazon Redshift MCP Server
Provides tools to discover, explore, and query Amazon Redshift clusters and serverless workgroups
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.
Workflows
Each server is designed for specific use cases:
- 👨💻 Vibe Coding & Development: AI coding assistants helping you build faster
- 💬 Conversational Assistants: Customer-facing chatbots and interactive Q&A systems
- 🤖 Autonomous Background Agents: Headless automation, ETL pipelines, and operational systems
Use Cases for the Servers
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.
Additional Resources
- Introducing AWS MCP Servers for code assistants
- Vibe coding with AWS MCP Servers | AWS Show & Tell
- 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
- Introducing AWS Serverless MCP Server: AI-powered development for modern applications