This guide helps you configure AWS MCP tools for AI agents. Two options are available:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionaws-mcp-setupExecute the skills CLI command in your project's root directory to begin installation:
Fetches aws-mcp-setup from zxkane/aws-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate aws-mcp-setup. Access via /aws-mcp-setup in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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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 |
Before configuring, check if AWS MCP tools are already available using either method:
Look for these tool name patterns in your agent's available tools:
mcp__aws-mcp__* or mcp__aws__* → Full AWS MCP Server configuredmcp__*awsdocs*__aws___* → AWS Documentation MCP configuredHow to check: Run /mcp command to list all active MCP servers.
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.
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"
Use when: uvx available AND AWS credentials valid
Prerequisites:
uv package managerRequired 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
Use when:
Configuration:
{
"mcpServers": {
"awsdocs": {
"type": "http",
"url": "https://knowledge-mcp.global.api.aws"
}
}
}
After configuration, verify tools are available:
For Full AWS MCP:
mcp__aws-mcp__aws___search_documentation, mcp__aws-mcp__aws___call_awsFor Documentation MCP:
mcp__awsdocs__aws___search_documentation, mcp__awsdocs__aws___read_documentation| 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 |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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aws-mcp-setup is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: aws-mcp-setup is focused, and the summary matches what you get after install.
Keeps context tight: aws-mcp-setup is the kind of skill you can hand to a new teammate without a long onboarding doc.
aws-mcp-setup reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added aws-mcp-setup from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for aws-mcp-setup matched our evaluation — installs cleanly and behaves as described in the markdown.
aws-mcp-setup fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
aws-mcp-setup reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for aws-mcp-setup matched our evaluation — installs cleanly and behaves as described in the markdown.
aws-mcp-setup has been reliable in day-to-day use. Documentation quality is above average for community skills.
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