How I Used Kiro + MCP to Document, Analyze, Improve and Visualize My AWS Linktree-Style Project
As an AWS Community Builder, I love experimenting with new developer workflows that combine automation, cloud architecture and AI tooling. This article describes how I used Kiro equipped with Model Context Protocol (MCP) servers to automatically analyze, document, diagram, audit and improve a real production project I run on AWS.
My Personal Project: A Fully Serverless "Linktree-Style" Website on AWS
Before diving into MCP, here’s the context.
I maintain a personal production project:
a Linktree-style website built entirely by me, deployed on AWS with:
S3 (static hosting, private bucket)
CloudFront (global delivery)
OAC (Origin Access Control)
CloudFront Functions (routing logic)
Route 53 (DNS and ALIAS records)
ACM Certificate (TLS)- GitHub Actions CI/CD
Terraform IaC (complete infrastructure automation)
This project has grown over time, and I wanted a faster — and smarter — way to:
- Understand and document the system
- Validate architecture decisions
- Receive AWS best-practice recommendations
- Generate diagrams using official AWS icons
- Improve Terraform (security, cost and performance)
- Inspect GitHub workflows and suggest enhancements
That’s when Kiro + MCP changed everything.
What Kiro + MCP Did for This Project
After installing five MCP servers, Kiro became a full cloud architecture assistant.
Here is exactly what it did:
1. Generated a complete Spec File
Kiro analyzed my GitHub repo, Terraform code, static files and folder structure.
Then it produced a clear, structured Spec File describing:
- System purpose
- AWS services used
- CI/CD workflow
- Deployment strategy
- Security posture
- Future improvements
This Spec became a “living blueprint” for the project.
2. It created a full AWS architecture diagram (Draw.io) by itself
Using the diagram MCP, Kiro generated a .drawio file with:
- Official AWS icons (mxgraph.aws4)
- S3, CloudFront, Route 53, IAM, OAC
- Data flows with arrow labels
- Component descriptions
- Legend and grouping
The diagram opens perfectly in app.diagrams.net.
3. Produced complete documentation using AWS official MCPs
Using:
awslabs.aws-documentation-mcp-serverawslabs.core-mcp-servermcp-server-fetchgithub MCPdiagram-ai MCP
Kiro:
- Looked up AWS best practices
- Compared my architecture to AWS recommendations
- Generated docs in clear, well-structured English
- Validated my CloudFront, S3 and Route 53 setup
- Suggested improvements using AWS Well-Architected
All with citations and references to AWS documentation pages.
4. Identified improvements and generated new Spec Files to implement them
Examples Kiro suggested (and generated code/specs for):
Security
- Add AWS WAF to CloudFront
- Enforce TLS 1.3
- Ensure S3 bucket has encryption + private access
- IAM improvements for GitHub OIDC
Logging
- Enable CloudFront logging
- Optimize TTLs
- Split long-cache and short-cache assets (I implemented this)
Performance
- Gzip/Brotli checks
- Image resizing recommendations
- Use CloudFront Functions instead of Lambda@Edge when possible
Kiro even generated Terraform snippets to apply each change.
My MCP Config (Workspace)
{
"mcpServers": {
"fetch": {
"command": "uvx",
"args": [
"mcp-server-fetch"
],
"env": {
"PYTHONIOENCODING": "utf-8",
"UV_PYTHON": "python3.12",
"UV_NO_LAUNCH": "1"
},
"disabled": false,
"autoApprove": [
"*",
"fetch"
]
},
"aws-docs": {
"command": "uv",
"args": [
"tool",
"run",
"--from",
"awslabs.aws-documentation-mcp-server@latest",
"awslabs.aws-documentation-mcp-server.exe"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"AWS_DOCUMENTATION_PARTITION": "aws",
"PYTHONIOENCODING": "utf-8",
"UV_PYTHON": "python3.12",
"UV_NO_LAUNCH": "1"
},
"disabled": false,
"autoApprove": [
"search_documentation"
]
},
"awslabs.core-mcp-server": {
"command": "uv",
"args": [
"tool",
"run",
"--from",
"awslabs.core-mcp-server@latest",
"awslabs.core-mcp-server.exe"
],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"PYTHONIOENCODING": "utf-8",
"UV_PYTHON": "python3.12",
"UV_NO_LAUNCH": "1"
},
"disabled": false
},
"github": {
"command": "C:\\Program Files\\nodejs\\npx.cmd",
"args": [
"-y",
"@modelcontextprotocol/server-github"
],
"env": {},
"disabled": false,
"autoApprove": [
"*",
"search_repositories",
"list_commits"
]
},
"diagram-ai": {
"command": "<!-- replace-here-->
\\python.exe",
"args": [
"-m",
"src.application.mcp.server_modular"
],
"env": {
"DIAGRAM_OUTPUT_DIR": "<!-- replace-here-->\\diagrams",
"PYTHONIOENCODING": "utf-8"
},
"disabled": false,
"autoApprove": ["*"]
}
}
}
AWS WAF Example (from the recommendations)
hcl
resource "aws_wafv2_web_acl" "site" {
provider = aws.us_east_1
name = "site-waf"
scope = "CLOUDFRONT"
rule {
name = "AWS-AWSManagedRulesCommonRuleSet"
priority = 1
override_action { none {} }
statement {
managed_rule_group_statement {
vendor_name = "AWS"
name = "AWSManagedRulesCommonRuleSet"
}
}
visibility_config {
cloudwatch_metrics_enabled = true
metric_name = "common-rules"
sampled_requests_enabled = true
}
}
}