MCP Servers to Find Your Most Expensive APIs.
API traffic metered, cache savings calculated, origin load measured, cost projections generated , optimize your API infrastructure costs with data
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads Datadog: 4.2M API requests in the last 7 days. Top endpoint: `/api/products` at 1.8M requests (43% of total traffic).
Average response time 45ms, average response size 2.1KB. Second: `/api/search` at 890K requests, average 340ms, average 8.4KB response. The agent reads Cloudflare: zone-level cache hit ratio is 72%.
But per-endpoint analysis shows `/api/products` has a 91% cache hit ratio , only 162K of 1.8M requests reach the origin.
While `/api/search` has a 12% cache hit ratio , 783K requests hit the origin because search queries are unique. The agent calculates: `/api/products` cache saved 1.64M origin requests 2.1KB = 3.4GB of origin bandwidth.
At $0.09/GB, that is $306 saved this week. `/api/search` could save more if you implement query normalization and cache similar searches.
Estimated saving: $180/week. It writes to Google Sheets: endpoint-level traffic, cache ratios, origin load, bandwidth costs, and optimization recommendations. After 4 weeks, you see which optimizations had the biggest impact.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect Datadog, Cloudflare and Google Sheets MCP servers so your AI agent reads API request volumes and latency from Datadog, calculates bandwidth savings from Cloudflare cache, compares edge vs. origin load, and generates a weekly infrastructure cost optimization report in Google Sheets. Platform teams running high-traffic APIs behind Cloudflare get data-driven cost optimization recommendations. No guessing which endpoints cost the most. No manual bandwidth calculations. One prompt and your API costs are visible.
Datadog
triggerReads API request volumes, latency and error rates per endpoint
query_metrics search_logs list_monitors list_hosts Cloudflare
actionReads cache analytics, bandwidth savings and edge performance
get_zone_analytics list_zones get_worker_analytics list_workers Google Sheets
actionWrites weekly cost reports and tracks optimization trends
append_sheet_values update_sheet_values get_spreadsheet create_spreadsheet Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own MCP
Turn any internal API into an MCP server. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Datadog, Cloudflare & Google Sheets ready in the catalog right now
- Add more from 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across every platform.
Contextual Reasoning
Every decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog to handle these for you. You describe what you need. It does the rest.
Platform teams running high-traffic APIs behind Cloudflare who need per-endpoint cost attribution and cache optimization data
Engineering managers presenting infrastructure cost reduction plans who need data-driven savings projections
FinOps teams tracking API infrastructure costs who need automated weekly reports without building custom dashboards
Startups approaching scale milestones who need to understand which endpoints will drive cost growth as traffic increases
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Three: Datadog, Cloudflare and Google Sheets. Connect all three to your AI client.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others.
How does the agent calculate costs?
The agent uses standard cloud bandwidth pricing ($0.09/GB) as a default. Customize in your prompt with your actual CDN and origin costs.
Can I use AWS CloudFront instead of Cloudflare?
This recipe uses the Cloudflare MCP. For CloudFront, you would need an AWS-specific MCP server for cache analytics.
Does it account for compute costs, not just bandwidth?
The base workflow tracks bandwidth. Add compute cost estimates by specifying your origin server cost per request in the prompt.
How often should I run this?
Weekly for cost tracking, monthly for optimization recommendations. Traffic patterns need at least a week of data for meaningful analysis.
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Monitor Deployment Health Using MCP Servers
Deployments tracked, latency spikes caught, error rates compared, rollback decisions made , monitor every ship without watching dashboards
MCP servers used in this workflow
Datadog
Datadog connects your AI agent directly to your infrastructure monitoring stack. Query performance metrics, search logs for specific errors, and check system monitor status using natural conversation. You get real-time visibility into application health without opening a dashboard.
Cloudflare
Cloudflare MCP Server manages your entire edge infrastructure via AI agents. Use it to deploy Workers, manage secrets, query D1 databases, and monitor traffic across KV, R2, and CDN—all from natural language commands.
Google Sheets
Google Sheets MCP Server lets your AI client read, write, and manage data directly in Google Sheets. Use conversational commands to pull data from specific ranges, append new rows, or structure entire spreadsheets. It acts as an analyst, letting you manipulate complex data without opening the GUI or writing formulas.