AWS MCP Setup Skill for Claude

Configure AWS MCP servers for documentation search and API access. Use when setting up AWS MCP, configuring AWS documentation tools, troubleshooting MCP connectivity, or when user mentions aws-mcp, awsdocs, uvx setup, or MCP server configuration. Covers both Full AWS MCP Server (with uvx + credentials) and lightweight Documentation MCP (no auth required).

1k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

342

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/zxkane/aws-skills --skill aws-mcp-setup

The instruction itself

11 sections, as written by the author

AWS MCP Server Configuration Guide

Overview

This guide helps you configure AWS MCP tools for AI agents. Two options are available:

| Option | Requirements | Capabilities |

|--------|--------------|--------------|

| Full AWS MCP Server | Python 3.10+, uvx, AWS credentials | Execute AWS API calls + documentation search |

| AWS Documentation MCP | None | Documentation search only |

Step 1: Check Existing Configuration

Before configuring, check if AWS MCP tools are already available using either method:

Method A: Check Available Tools (Recommended)

Look for these tool name patterns in your agent's available tools:

mcp__aws-mcp__* or mcp__aws__* → Full AWS MCP Server configured
mcp__*awsdocs*__aws___* → AWS Documentation MCP configured

How to check: Run /mcp command to list all active MCP servers.

Method B: Check Configuration Files

Agent tools use hierarchical configuration (precedence: local → project → user → enterprise):

| Scope | File Location | Use Case |

|-------|---------------|----------|

| Local | .claude.json (in project) | Personal/experimental |

| Project | .mcp.json (project root) | Team-shared |

| User | ~/.claude.json | Cross-project personal |

| Enterprise | System managed directories | Organization-wide |

Check these files for mcpServers containing aws-mcp, aws, or awsdocs keys:

# Check project config
cat .mcp.json 2>/dev/null | grep -E '"(aws-mcp|aws|awsdocs)"'

# Check user config
cat ~/.claude.json 2>/dev/null | grep -E '"(aws-mcp|aws|awsdocs)"'

# Or use Claude CLI
claude mcp list

If AWS MCP is already configured, no further setup needed.

Step 2: Choose Configuration Method

Automatic Detection

Run these commands to determine which option to use:

# Check for uvx (requires Python 3.10+)
which uvx || echo "uvx not available"

# Check for valid AWS credentials
aws sts get-caller-identity || echo "AWS credentials not configured"

Option A: Full AWS MCP Server (Recommended)

Use when: uvx available AND AWS credentials valid

Prerequisites:

Python 3.10+ with uv package manager
AWS credentials configured (via profile, environment variables, or IAM role)

Required IAM Permissions:

{
  "Version": "2012-10-17",
  "Statement": [{
    "Effect": "Allow",
    "Action": [
      "aws-mcp:InvokeMCP",
      "aws-mcp:CallReadOnlyTool",
      "aws-mcp:CallReadWriteTool"
    ],
    "Resource": "*"
  }]
}

Configuration (add to your MCP settings):

{
  "mcpServers": {
    "aws-mcp": {
      "command": "uvx",
      "args": [
        "mcp-proxy-for-aws@latest",
        "https://aws-mcp.us-east-1.api.aws/mcp",
        "--metadata", "AWS_REGION=us-west-2"
      ]
    }
  }
}

Credential Configuration Options:

AWS Profile (recommended for development):

   "args": [
     "mcp-proxy-for-aws@latest",
     "https://aws-mcp.us-east-1.api.aws/mcp",
     "--profile", "my-profile",
     "--metadata", "AWS_REGION=us-west-2"
   ]

Environment Variables:

   "env": {
     "AWS_ACCESS_KEY_ID": "...",
     "AWS_SECRET_ACCESS_KEY": "...",
     "AWS_REGION": "us-west-2"
   }

IAM Role (for EC2/ECS/Lambda): No additional config needed - uses instance credentials

Additional Options:

--region <region>: Override AWS region
--read-only: Restrict to read-only tools
--log-level <level>: Set logging level (debug, info, warning, error)

Reference: https://github.com/aws/mcp-proxy-for-aws

Option B: AWS Documentation MCP Server (No Auth)

Use when:

No Python/uvx environment
No AWS credentials
Only need documentation search (no API execution)

Configuration:

{
  "mcpServers": {
    "awsdocs": {
      "type": "http",
      "url": "https://knowledge-mcp.global.api.aws"
    }
  }
}

Step 3: Verification

After configuration, verify tools are available:

For Full AWS MCP:

Look for tools: mcp__aws-mcp__aws___search_documentation, mcp__aws-mcp__aws___call_aws

For Documentation MCP:

Look for tools: mcp__awsdocs__aws___search_documentation, mcp__awsdocs__aws___read_documentation

Troubleshooting

| Issue | Cause | Solution |

|-------|-------|----------|

| uvx: command not found | uv not installed | Install with pip install uv or use Option B |

| AccessDenied error | Missing IAM permissions | Add aws-mcp:* permissions to IAM policy |

| InvalidSignatureException | Credential issue | Check aws sts get-caller-identity |

| Tools not appearing | MCP not started | Restart your agent after config change |

Other skills for the same job

different authors, same section of the catalogue

Azure Kubernetes Automatic Readiness

by microsoft

vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take zxkane/aws-mcp-setup from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip, uvx. Without those the skill loads but fails at the first command.