MCP server
by awslabs
Access and search AWS documentation easily with this AWS document management solution, including WorkDocs tools and reco
Provides direct access to AWS documentation through search, page retrieval, and content recommendations. Lets you query and browse AWS docs without leaving your development environment.
AWS Documentation is an official MCP server published by awslabs that provides AI assistants with tools and capabilities via the Model Context Protocol. Access and search AWS documentation easily with this AWS document management solution, including WorkDocs tools and reco This server exposes 3 tools that AI clients can invoke during conversations and coding sessions.
You can install AWS Documentation in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport. This server supports remote connections over HTTP, so no local installation is required.
Apache-2.0
AWS Documentation is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
AWS Documentation is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired AWS Documentation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
AWS Documentation reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We evaluated AWS Documentation against two servers with overlapping tools; this profile had the clearer scope statement.
AWS Documentation is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
AWS Documentation is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated AWS Documentation against two servers with overlapping tools; this profile had the clearer scope statement.
We wired AWS Documentation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
I recommend AWS Documentation for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Useful MCP listing: AWS Documentation is the kind of server we cite when onboarding engineers to host + tool permissions.
showing 1-10 of 30
A suite of specialized MCP servers that help you get the most out of AWS, wherever you use MCP.
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
The MCP protocol currently defines two standard transport mechanisms for client-server communication:
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.
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.
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.
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:
For AWS interactions, we recommend starting with:
| Server Name | Description | Install |
|---|---|---|
| [AWS MCP Server (in preview)](https://docs.aws.ama |
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
