AI/TLDR

AWS MCP Servers

AWS Labs' suite of MCP servers for docs, infrastructure and data

Workflow AutomationOpen source
Language
Python
License
Apache-2.0
$uvx awslabs.aws-documentation-mcp-server@latest

Overview

AWS MCP Servers is AWS Labs' official collection of Model Context Protocol servers for working with AWS from an AI client. Rather than one monolithic server, the repository publishes many focused ones — each a separate package you run on demand — so an agent only ever gets the surface you deliberately hand it.

The suite spans several areas. Infrastructure and deployment servers cover infrastructure-as-code, CloudFormation, CDK, EKS and ECS. Data and analytics servers cover DynamoDB, Aurora (PostgreSQL, MySQL and DSQL), DocumentDB, Neptune and Redshift. AI and machine-learning servers cover Bedrock, Kendra, Amazon Q and SageMaker. There are also developer-tool servers for IAM, CloudWatch and pricing analysis, integration servers for SNS/SQS, Step Functions and Location Services, and a healthcare and life-sciences group covering HealthOmics, HealthImaging and HealthLake.

The one most people start with is the AWS Documentation server, which gives an agent grounded access to current AWS docs — a direct answer to models confidently citing API shapes that changed. Servers are run with `uvx` straight from the package index, so there is nothing to clone, and they use your existing AWS credentials and profile configuration.

What it does

  • Many single-purpose servers rather than one broad one, so you scope an agent to exactly the AWS surface it needs
  • AWS Documentation server for grounded, current answers about AWS APIs and services
  • Infrastructure and deployment: IaC, CloudFormation, CDK, EKS and ECS
  • Data and analytics: DynamoDB, Aurora PostgreSQL/MySQL/DSQL, DocumentDB, Neptune and Redshift
  • AI and ML: Bedrock, Kendra, Amazon Q and SageMaker; plus IAM, CloudWatch and pricing analysis
  • Healthcare and life sciences: HealthOmics, HealthImaging and HealthLake
  • Run on demand with uvx — no clone or install step — using your existing AWS credentials

Getting started

Every server follows the same pattern: run it with uvx by package name, then register that command in your MCP client. Standard AWS credentials and profile configuration apply.

Run a server

Servers are published as awslabs.<server-name>. The documentation server is the usual starting point.

bashbash
uvx awslabs.aws-documentation-mcp-server@latest

Register it with your MCP client

For VS Code, add it to .vscode/mcp.json. Other clients take the same command and args in their own config file.

jsonjson
{
  "command": "uvx",
  "args": ["awslabs.aws-documentation-mcp-server@latest"],
  "env": {
    "FASTMCP_LOG_LEVEL": "ERROR",
    "AWS_DOCUMENTATION_PARTITION": "aws"
  }
}

Add further servers as you need them

Swap the package name for any other server in the suite — the install and configuration shape is identical, which is what makes running several side by side practical.

bashbash
uvx awslabs.[server-name]@latest

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

When to use it

  • Reach for it when an agent keeps inventing AWS API shapes — the documentation server grounds it in the real docs
  • Reach for it to let an assistant draft and inspect CDK or CloudFormation stacks against your actual account
  • Reach for it to give an agent scoped read access to operational data in DynamoDB, Aurora or CloudWatch
  • Reach for it when you want per-service isolation: one server per capability instead of one credential that opens everything

How AWS MCP Servers compares

AWS MCP Servers alongside other open-source workflow automation tools AI/TLDR tracks, ranked by GitHub stars.

ToolStarsWhat it does
n8n★ 205kA self-hostable workflow automation tool with a visual node editor that connects 400+ apps and APIs and adds native AI steps, letting technical teams build automations without writing most of the glue code.
Huginn★ 50kA self-hosted system of "agents" that watch websites, feeds, and events and take automated actions on your behalf, similar to a private IFTTT.
GitHub MCP Server★ 33kGitHub's official MCP server, hosted or self-run, that gives an agent scoped toolsets for repositories, issues, pull requests, Actions runs and code-security alerts.
gws (Google Workspace CLI)★ 31kA command-line tool for every Google Workspace API, built at runtime from Google’s Discovery Service, returning structured JSON and shipping 40+ agent skills. Not an officially supported Google product.
Kestra★ 28.1kAn event-driven orchestration platform that defines data, AI, and infrastructure pipelines as declarative YAML and runs them through a web UI.
Activepieces★ 24.5kAn open-source Zapier alternative where you build automations from reusable TypeScript "pieces", with support for AI agents and self-hosting.
n8n-MCP★ 22.9kAn MCP server that gives coding assistants structured access to n8n's node catalogue, schemas, docs and workflow templates so they can build and validate n8n workflows.
AWS MCP Servers★ 9.7kAWS Labs' suite of MCP servers for docs, infrastructure and data