aws-mcp-setup▌
zxkane/aws-skills · updated Apr 8, 2026
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This guide helps you configure AWS MCP tools for AI agents. Two options are available:
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__*ormcp__aws__*→ Full AWS MCP Server configuredmcp__*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
uvpackage 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 |
How to use aws-mcp-setup on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add aws-mcp-setup
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches aws-mcp-setup from GitHub repository zxkane/aws-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate aws-mcp-setup. Access the skill through slash commands (e.g., /aws-mcp-setup) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★28 reviews- ★★★★★Sakura Abebe· Dec 20, 2024
aws-mcp-setup is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Evelyn Mensah· Dec 16, 2024
Solid pick for teams standardizing on skills: aws-mcp-setup is focused, and the summary matches what you get after install.
- ★★★★★Ganesh Mohane· Dec 12, 2024
Keeps context tight: aws-mcp-setup is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Hana Mehta· Nov 11, 2024
aws-mcp-setup reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yuki Kapoor· Nov 7, 2024
We added aws-mcp-setup from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Rahul Santra· Nov 3, 2024
Registry listing for aws-mcp-setup matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★William Mensah· Oct 26, 2024
aws-mcp-setup fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Pratham Ware· Oct 22, 2024
aws-mcp-setup reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kofi Abbas· Oct 2, 2024
Registry listing for aws-mcp-setup matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Piyush G· Sep 13, 2024
aws-mcp-setup has been reliable in day-to-day use. Documentation quality is above average for community skills.
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