Connectors That Stop Unnecessary Deploys.
Deployment strategy derived from physics, buzzwords purged, assumptions challenged , ship infrastructure decisions grounded in axioms, not analogies
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads next.config.js and vercel.json: the team configured ISR with 60-second revalidation on all 2,400 product pages, edge functions for API routes, and stale-while-revalidate cache headers.
The agent runs `validate_first_principles`. Discard Analogies: 'We use ISR because Vercel recommends it' is an analogy , what is the AXIOM? Isolate Constraints: the fundamental constraint is data freshness.
Product prices change on average once per week. Fundamental truth: data that changes weekly does not need 60-second revalidation. Derive: optimal revalidation = max_acceptable_staleness safety_factor.
For weekly price changes: revalidation = 3600s (1 hour) with on-demand revalidation on price update webhook. This reduces ISR invocations by 60x.
Prove: at 2,400 pages 60s revalidation = 2,400 rebuilds/minute. At 3,600s = 40 rebuilds/minute. Compute cost reduction: 98.3%. Filter: 'edge functions for optimal DX' is a buzzword.
Axiom: edge functions add cold start latency (50-200ms). For API routes called once per user session, the cold start cost exceeds the latency benefit.
Move to serverless functions. The agent updates Vercel project configuration accordingly.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect GitHub, First Principles Prover and Vercel Connectors so your AI agent reads your deployment configuration, forces every infrastructure decision through six axiom-based proofs (discard analogies, isolate constraints, derive from math, validate with proof, filter buzzwords), and configures Vercel deployments based on proven axioms rather than copied patterns. Teams deploying because 'that is how everyone does it' get a first-principles derivation that proves whether their edge functions, ISR strategy, and caching headers are correct for THEIR specific traffic patterns. No copying Vercel templates. One prompt and the agent derives the optimal deployment from the physics of your system.
Github
triggerReads deployment configs, Next.js settings, caching strategies, and traffic patterns
get_file_contents search_github_code list_user_repositories First Principles Prover
actionForces six-pivot axiom validation: discard analogies, isolate constraints, derive from math, prove, filter jargon
validate_first_principles Vercel
actionConfigures deployments, environment variables, and edge functions based on proven axioms
vercel_list_projects vercel_get_project vercel_list_deployments 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 Connector
Convert any internal API into a Connector. 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 each 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.
- Github, First Principles Prover & Vercel ready in the catalog right now
- Add more from 5,800+ servers whenever you need
- Connections are secured and compliant by default
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added weekly
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 5,800+ Connectors and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across all tools, in one conversation. That's what this connectivity layer 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 all platforms.
Contextual Reasoning
Each 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 Connectors once, and your agent handles the rest. Each time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius AI Connectors to handle these for you. You describe what you need. It does the rest.
Frontend teams deploying Next.js to Vercel who want deployment configurations derived from their actual traffic, not copied from templates
Platform engineers optimizing Vercel costs who need mathematical proof of which configurations reduce compute without impacting performance
Startups scaling from prototype to production who need first-principles guidance on ISR revalidation, caching, and edge function placement
Engineering managers who suspect their team is cargo-culting deployment patterns and want axiom-based validation of every infrastructure choice
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: GitHub, First Principles Prover and Vercel. 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.
Will the agent modify my Vercel deployment?
The agent reads your current configuration and provides axiom-based recommendations. You review and apply the changes.
Does this only work with Next.js?
The First Principles Prover is framework-agnostic. It derives optimal deployment from your data physics. Works with any framework deployed to Vercel.
What does ANALOGY_DETECTED mean?
Your decision was based on copying someone else rather than deriving from your own system's axioms. The Prover forces you to replace the analogy with a mathematical proof.
How much can this save on Vercel costs?
Depends on your current configuration. Teams with aggressive ISR revalidation on low-change-frequency pages typically see 90-98% reduction in unnecessary rebuilds.
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Connectors used in this workflow
GitHub
GitHub MCP lets you manage your entire software development lifecycle through a chat interface. You can check the status of a pull request, list open issues, or search for specific code snippets without ever leaving your primary workspace. It gives your AI agent direct access to your repositories, making it easier to audit codebases or update project statuses on the fly.
First Principles Prover
First Principles Prover is a cognitive forcing MCP that prevents your AI client from relying on industry tropes. It forces the agent to strip away 'best practices' and derive original solutions from raw physical, mathematical, or logical axioms. Use it when you need high-stakes reasoning that isn't just a copy of what everyone else is doing.
Vercel
Vercel MCP brings your deployment infrastructure into your chat. Control project domains, trigger manual builds, and inspect deployment status natively without leaving your IDE. It connects your CI/CD pipeline to your AI client for faster hotfixes and environment management.