Cache headers, CDN, LRU simulation for AI agents
Valid MCP server (5 strong, 4 medium validity signals). 3 known CVEs in dependencies (0 critical, 3 high severity) Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-rog0x-cache": {
"args": [
"-y",
"@rog0x/mcp-cache-tools"
],
"command": "npx"
}
}
}From the project's GitHub README.
MCP Server #30 — Caching and memoization tools for AI agents.
Analyze, generate, simulate, and optimize caching strategies across HTTP, CDN, and browser layers.
analyze_cache_headersAnalyze HTTP cache headers (Cache-Control, ETag, Last-Modified, Expires, Vary). Grades the overall caching strategy A+ through F and provides actionable improvement suggestions with context-aware advice for static assets, API responses, HTML pages, and media.
generate_cache_keyGenerate deterministic cache keys from various inputs:
Supports namespacing, versioning, TTL, and outputs ready-to-use Redis/Memcached commands.
simulate_cacheSimulate cache behavior for a sequence of requests. Compare eviction strategies side-by-side:
Outputs hit rate, miss rate, eviction count, and step-by-step timeline.
check_cdn_headersDetect which CDN is serving a resource from response headers. Identifies Cloudflare, Fastly, Akamai, CloudFront, Vercel, Netlify, Google Cloud CDN, Azure CDN, KeyCDN, and Bunny CDN. Reports cache status (HIT/MISS/STALE/BYPASS), edge server, and POP location.
analyze_browser_cacheAnalyze what resources a page would cache in the browser. Determines cache type (strong, heuristic, conditional, none), duration, revalidation strategy, and estimates bandwidth/time savings on repeat visits. Groups results by resource type with optimization suggestions.
npm install
npm run build
{
"mcpServers": {
"mcp-cache-tools": {
"command": "node",
"args": ["D:/products/mcp-servers/mcp-cache-tools/dist/index.js"]
}
}
}
MIT
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