AWS MCP Servers

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?
- Why MCP Servers?
Available Servers
Installation and Setup
Samples
Documentation
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.
— 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 includ 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 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 Servers

This monorepo contains the following MCP servers:

Core MCP Server

A server for managing and coordinating other AWS Labs MCP servers.

Automatic MCP Server Management
Planning and guidance to orchestrate AWS Labs MCP Servers
UVX Installation Support
Centralized Configuration

Learn more | Documentation

AWS Documentation MCP Server

A server for accessing AWS documentation and best practices.

Search Documentation using the official AWS search API
Get content recommendations for AWS documentation pages
Convert documentation to markdown format

Learn more | Documentation

Amazon Bedrock Knowledge Bases Retrieval MCP Server

A server for accessing Amazon Bedrock Knowledge Bases.

Discover knowledge bases and their data sources
Query knowledge bases with natural language
Filter results by data source
Rerank results

Learn more | Documentation

AWS CDK MCP Server

A server for AWS CDK best practices.

AWS CDK project analysis and assistance
CDK construct recommendations
Infrastructure as Code best practices

Learn more | Documentation

Cost Analysis MCP Server

A server for AWS Cost Analysis.

Analyze and visualize AWS costs
Query cost data with natural language
Generate cost reports and insights

Learn more | Documentation

Amazon Nova Canvas MCP Server

A server for generating images using Amazon Nova Canvas.

Text-based image generation with customizable parameters
Color-guided image generation with specific palettes
Workspace integration for saving generated images
AWS authentication through profiles

Learn more | Documentation

AWS Diagram MCP Server

A server for seamlessly creating diagrams using the Python diagrams package DSL.

Generate professional diagrams using Python code
Support for AWS architecture, sequence diagrams, flow charts, and class diagrams
Customize diagram appearance, layout, and styling
Code scanning to ensure secure diagram generation

Learn more | Documentation

AWS Lambda MCP Server

An server to select and run AWS Lambda function as MCP tools without code changes.

This server acts as a bridge between MCP clients and AWS Lambda functions, allowing foundation models (FMs) to access and run Lambda functions as tools.
This can be used, for example, to access private resources such as internal applications and databases without the need to provide public network access.
This approach allows an MCP client to use other AWS services, private networks, and the public internet.
The Lambda function description is used by MCP to describe the tool and should guide the FMs on when (what does the function provide?) and how (which parameters it needs? which syntax?) to use it.

Learn more | Documentation

AWS Terraform MCP Server

A server for AWS Terraform best practices.

Security-First Development Workflow
Checkov Integration
AWS and AWSCC Provider Documentation
AWS-IA GenAI Modules
Terraform Workflow Execution

Learn more | Documentation

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 are my top 3 AWS services by cost last month?” or “How can I reduce my EC2 costs?” and receive detailed insights and actionable recommendations.

Installation and Setup

Each server has specific installation instructions. Generally, you can:

Install uv from 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):

{
  "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"
      }
    },
    "awslabs.nova-canvas-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.nova-canvas-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      }
    },
    "awslabs.bedrock-kb-retrieval-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.bedrock-kb-retrieval-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      }
    },
    "awslabs.cost-analysis-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.cost-analysis-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "FASTMCP_LOG_LEVEL": "ERROR"
      }
    },
    "awslabs.cdk-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.cdk-mcp-server@latest"],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR"
      }
    },
    "awslabs.aws-documentation-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.aws-documentation-mcp-server@latest"],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "disabled": false,
      "autoApprove": []
    },
    "awslabs.lambda-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.lambda-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1",
        "FUNCTION_PREFIX": "your-function-prefix",
        "FUNCTION_LIST": "your-first-function, your-second-function",
        "FUNCTION_TAG_KEY": "your-tag-key",
        "FUNCTION_TAG_VALUE": "your-tag-value"
      }
    }
  }
}

See individual server READMEs for specific requirements and configuration options.

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.

Documentation

Comprehensive documentation for all servers is available on our documentation website.

Documentation for each server:

Documentation includes:

Detailed guides for each server
Installation and configuration instructions
API references
Usage examples

Additional Resources

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

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