AWS Documentation
OfficialProvides direct access to AWS documentation through search, page retrieval, and content recommendations. Lets you query and browse AWS docs without leaving your development environment.
Provides tools to access AWS documentation, search for content, and get recommendations.
What it does
- Search across all AWS documentation
- Fetch AWS documentation pages as markdown
- Get recommendations for related AWS content
- Convert AWS docs to readable format
Best for
About AWS Documentation
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.
How to install
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.
License
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.
Tools (3)
Fetch and convert an AWS documentation page to markdown format. ## Usage This tool retrieves the content of an AWS documentation page and converts it to markdown format. For long documents, you can make multiple calls with different start_index values to retrieve the entire content in chunks. ## URL Requirements - Must be from the docs.aws.amazon.com domain - Must end with .html ## Example URLs - https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html - https://docs.aws.amazon.com/lambda/latest/dg/lambda-invocation.html ## Output Format The output is formatted as markdown text with: - Preserved headings and structure - Code blocks for examples - Lists and tables converted to markdown format ## Handling Long Documents If the response indicates the document was truncated, you have several options: 1. **Continue Reading**: Make another call with start_index set to the end of the previous response 2. **Stop Early**: For very long documents (>30,000 characters), if you've already found the specific information needed, you can stop reading Args: ctx: MCP context for logging and error handling url: URL of the AWS documentation page to read max_length: Maximum number of characters to return start_index: On return output starting at this character index Returns: Markdown content of the AWS documentation
Search AWS documentation using the official AWS Documentation Search API. ## Usage This tool searches across all AWS documentation for pages matching your search phrase. Use it to find relevant documentation when you don't have a specific URL. ## Search Tips - Use specific technical terms rather than general phrases - Include service names to narrow results (e.g., "S3 bucket versioning" instead of just "versioning") - Use quotes for exact phrase matching (e.g., "AWS Lambda function URLs") - Include abbreviations and alternative terms to improve results - Use guide_type and product_type filters found from a SearchResponse's "facets" property: - Filter only for broad search queries with patterns: - "What is [service]?" -> product_types: ["Amazon Simple Storage Service"] - "How to use <service 1> with <service 2>?" -> product_types: [<service 1>, <service 2>] - "[service] getting started" -> product_types: [<service>] + guide_types: ["User Guide, "Developer Guide"] - "API reference for [service]" -> product_types: [<service>] + guide_types: ["API Reference"] ## Result Interpretation Each SearchResponse includes: - search_results: List of documentation pages, each with: - rank_order: The relevance ranking (lower is more relevant) - url: The documentation page URL - title: The page title - context: A brief excerpt or summary (if available) - facets: Available filters (product_types, guide_types) for refining searches - query_id: Unique identifier for this search session Args: ctx: MCP context for logging and error handling search_phrase: Search phrase to use search_intent: The intent behind the search requested by the user limit: Maximum number of results to return product_types: Filter by AWS product/service guide_types: Filter by guide type Returns: List of search results with URLs, titles, query ID, context snippets, and facets for filtering
Get content recommendations for an AWS documentation page. ## Usage This tool provides recommendations for related AWS documentation pages based on a given URL. Use it to discover additional relevant content that might not appear in search results. ## Recommendation Types The recommendations include four categories: 1. **Highly Rated**: Popular pages within the same AWS service 2. **New**: Recently added pages within the same AWS service - useful for finding newly released features 3. **Similar**: Pages covering similar topics to the current page 4. **Journey**: Pages commonly viewed next by other users ## When to Use - After reading a documentation page to find related content - When exploring a new AWS service to discover important pages - To find alternative explanations of complex concepts - To discover the most popular pages for a service - To find newly released information by using a service's welcome page URL and checking the **New** recommendations ## Finding New Features To find newly released information about a service: 1. Find any page belong to that service, typically you can try the welcome page 2. Call this tool with that URL 3. Look specifically at the **New** recommendation type in the results ## Result Interpretation Each recommendation includes: - url: The documentation page URL - title: The page title - context: A brief description (if available) Args: ctx: MCP context for logging and error handling url: URL of the AWS documentation page to get recommendations for Returns: List of recommended pages with URLs, titles, and context
Open source MCP servers for AWS
A suite of specialized MCP servers that help you get the most out of AWS, wherever you use MCP.
Table of Contents
- Open source MCP servers for AWS
- Table of Contents
- What is the Model Context Protocol (MCP) and how does it work with MCP Servers for AWS?
- Open source MCP servers for AWS Transport Mechanisms
- Why MCP Servers for AWS?
- 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 MCP Servers for AWS?
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
Open source MCP servers for AWS 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
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.
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 MCP Servers for AWS?
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.
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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.
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 Name | Description | Install |
|---|---|---|
| [AWS MCP Server (in preview)](https://docs.aws.ama |
README truncated. View full README on GitHub.
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