CloudFront Cache Analyzer Connector for AI agents.
4 live capabilities
Optimize AWS CloudFront hit ratios and reduce origin load
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Why people use CloudFront Cache Analyzer
Fixing AWS CloudFront cache misses with CloudFront Cache Analyzer
With this MCP, you stop guessing. You feed your metrics to your agent, and it performs the heavy lifting. You get immediate, deterministic answers on hit ratios and origin load, turning a tedious manual audit into a quick conversation.
What Vinkius changes
Get the CloudFront Cache Analyzer answer you need in the AI client you already use.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,400+ Connectors
- Real-world use case 01
Reducing unexpected origin spikes
An engineer notices the origin server is overwhelmed.
- Real-world use case 02
Cutting AWS data transfer costs
A cloud architect wants to lower costs.
- Real-world use case 03
Improving resilience during outages
To prevent errors when the origin is slow, an SRE uses calculate_stale_windows to define the right stale-while-revalidate period.
Complete set · 4capabilities
The complete CloudFront Cache Analyzer capability set.
These are the exact actions your AI can choose when you ask it to work with CloudFront Cache Analyzer.
01—04
4 capabilities in this set.
Part of 4 available through CloudFront Cache Analyzer.
- 01 Capability
Analyze path patterns
Identifies how caching behavior varies across different URL paths. It helps you find specific directories where cache hits are low.
- 02 Capability
Get ttl optimization advice
Suggests specific TTL adjustments to improve your hit ratio. It helps you balance content freshness with origin offload.
- 03 Capability
Calculate cache metrics
Computes hit ratios, origin load reduction, and bandwidth delivery. It provides the hard numbers for your current setup.
- 04 Capability
Calculate stale windows
Calculates optimal SWR and SIE header values. This improves availability and reduces latency during revalidation.
Set up in minutes
One URL. Then ask CloudFront Cache Analyzer to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use CloudFront Cache Analyzer from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_pY3JBkhofLj9ZJ2P3hKHE0aLobhTrObLRMqTEYMH/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it CloudFront Cache Analyzer, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable CloudFront Cache Analyzer for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_pY3JBkhofLj9ZJ2P3hKHE0aLobhTrObLRMqTEYMH/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the CloudFront Cache Analyzer URL.
- Step 03
Save and start
Save the connection and enable CloudFront Cache Analyzer in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"cloudfront-cache-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_pY3JBkhofLj9ZJ2P3hKHE0aLobhTrObLRMqTEYMH/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using CloudFront Cache Analyzer
Open Agent mode in chat and ask: "Using CloudFront Cache Analyzer, help me...". 4 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"cloudfront-cache-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_pY3JBkhofLj9ZJ2P3hKHE0aLobhTrObLRMqTEYMH/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using CloudFront Cache Analyzer
Ask Copilot: "Using CloudFront Cache Analyzer, help me...". 4 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"cloudfront-cache-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_pY3JBkhofLj9ZJ2P3hKHE0aLobhTrObLRMqTEYMH/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using CloudFront Cache Analyzer
Open Cascade and ask: "Using CloudFront Cache Analyzer, help me...". 4 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"cloudfront-cache-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_pY3JBkhofLj9ZJ2P3hKHE0aLobhTrObLRMqTEYMH/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using CloudFront Cache Analyzer
Ask Cline: "Using CloudFront Cache Analyzer, help me...". 4 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add cloudfront-cache-analyzer --transport http "https://edge.vinkius.com/vk_preview_pY3JBkhofLj9ZJ2P3hKHE0aLobhTrObLRMqTEYMH/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using CloudFront Cache Analyzer
Ask Claude: "Using CloudFront Cache Analyzer, show me...". 4 tools are ready
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Bring your own AI
Change the model, client or framework. Keep CloudFront Cache Analyzer connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
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Vercel AI SDK
Before you connect
Questions about CloudFront Cache Analyzer.
The practical details behind the request, access and result.
How can I use CloudFront Cache Analyzer to lower my AWS bill?
You can use this MCP to calculate exactly how much bandwidth you'll save by increasing your TTLs. It identifies the specific improvements needed to offload more traffic from your origin to the edge.
Can CloudFront Cache Analyzer help with site reliability?
Yes. By using the stale window calculations, you can determine the best SWR and SIE headers to keep your content available to users even if your origin server experiences a temporary outage.
How does CloudFront Cache Analyzer improve my cache hit ratio?
It analyzes your current TTL settings and path behaviors to provide specific advice on how to adjust your configuration to capture more requests at the edge.
Is CloudFront Cache Analyzer useful for debugging specific URL paths?
Absolutely. It includes capabilities to evaluate how caching behavior differs across various URL patterns, helping you find exactly which paths are causing cache misses.
Can I use CloudFront Cache Analyzer with Claude or Cursor?
Yes. Once you connect this MCP through Vinkius, you can use it directly within any compatible AI client like Claude, Cursor, or Windsurf.
One connection away
Give your agent a direct line to CloudFront Cache Analyzer.
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