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AWS Documentation

BYawslabs★ 7,894GRADE B
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Scan checks include:

  • Unbounded filesystem access & path traversal
  • Dangerous eval/exec or shell usage
  • Hardcoded secrets, tokens, or credential leaks
  • Unexpected network calls or untrusted endpoints
  • Suspicious install scripts and dependency risks

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Fetch, convert, and search AWS documentation pages, with recommendations for related content.

View Source on GitHub

⚡Config Installation

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "aws-documentation": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-aws-documentation"
      ]
    }
  }
}
Install command: npx -y @modelcontextprotocol/server-aws-documentation

* Note: Requires restart of Claude Desktop app.

View Source Code→

Deployment Infrastructure

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🛡️ Security Analysis

SCANNED: 2026-01-18
SCORE: 80/100
✅

Clean Scan Report

Our static analysis engine detected no common vulnerabilities (RCE, API Leaks, Unbounded FS).

DocumentationREADME.md

Note: The content below is automatically rendered from the repository's README file.

AWS MCP Servers

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

GitHub License Codecov OSSF-Scorecard Score

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 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.

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

AWS MCP Servers 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

These AWS MCP Servers 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 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 AWS interactions, we recommend starting with:

Server NameDescriptionInstall
AWS MCP ServerStart here for complete 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, Getting Started information, and brings support for Agent standard operat

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