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Connect Kubernetes Architecture Prover to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Stop deploying production-breaking manifests with your AI agent.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 1 capability

We ship only the actions that matter.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through Kubernetes Architecture Prover.

  1. 01

    Validate kubernetes architecture

    This capability validates Kubernetes workloads against five strict architectural pillars: resource governance, security hardening, reliability design, observability, and network restriction. It cross-checks your agent's specific parameters against its final verdict to catch self-contradictory or incomplete configurations.

Real work

What teams actually run

01

Automated Manifest Review

Pass an agent-generated YAML through the MCP to check for missing resource limits or security flaws.

02

Production Readiness Audits

Use the capability to verify if a new service design meets reliability and observability standards.

03

Security Hardening

Ensure all workloads comply with restricted PodSecurityStandards and non-root requirements.

04

Network Policy Generation

Force the agent to move away from flat networks toward explicit deny-all policies.

Automate a task

Put Kubernetes Architecture Prover to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Kubernetes Architecture Prover works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Kubernetes Architecture Prover to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

971ms average. Fast in production.

Kubernetes Architecture Prover is checked daily against the live service.

Daily averagePeak 1143ms
Oct 3Today
Fastest day
818ms
Slowest day
1143ms
14-day trend
Slowing+11%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 1 capability arrives ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Kubernetes Architecture Prover, so you can see the experience inside your AI.

It does not authenticate your account with Kubernetes Architecture Prover. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Kubernetes Architecture Prover Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_5R8kMC05RFsYvzrADI0onwKUjFTA7YO6YqsJzlol/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Kubernetes Architecture Prover capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "kubernetes-architecture-prover-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_5R8kMC05RFsYvzrADI0onwKUjFTA7YO6YqsJzlol/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Kubernetes Architecture Prover in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Kubernetes Architecture Prover owners hand off.

Platform engineers and SREs who use AI agents to automate Kubernetes manifest generation.

  • 01

    Platform Engineer

    Uses the MCP to ensure AI-generated manifests meet organizational guardrails.

  • 02

    SRE

    Employs the capability to prevent single points of failure and resource exhaustion in production.

  • 03

    DevOps Engineer

    Hands off manifest validation to the agent to catch security holes before deployment.

FAQ

Questions Kubernetes Architecture Prover owners ask.

  • 01

    What happens if my AI agent skips resource limits?

    The MCP will return a RESOURCES_UNGOVERNED verdict. It will explain that without limits, noisy neighbors can OOM-kill your workloads and the scheduler cannot make informed decisions.

  • 02

    Can this MCP prevent container escapes?

    Yes. By validating security hardening, it ensures containers run as non-root, drop capabilities, and follow restricted PodSecurityStandards.

  • 03

    Does it support multi-replica deployments?

    Yes. It checks for reliability design, including minimum replica counts, PodDisruptionBudgets, and anti-affinity rules to prevent single points of failure.

  • 04

    How does it handle network security?

    It rejects flat network configurations. It requires a default deny-all NetworkPolicy with explicit allow rules for ingress and egress.

  • 05

    What is a 'Decision Pivot'?

    A Decision Pivot is one of the five core architectural categories (Resources, Security, Reliability, Observability, Networking) used to judge the quality of a workload.

  • 06

    Does it generate Kubernetes manifests?

    No. It validates that your architecture addresses the five production-critical pillars. resource governance, security hardening, reliability design, observability instrumentation, and network restriction. It does not generate YAML. It forces you to prove your YAML is production-ready.

  • 07

    What counts as proper resource governance?

    Every container must have CPU and memory requests AND limits. Every namespace must have a LimitRange (defaults for containers that don't specify) and a ResourceQuota (ceiling for the namespace). 'The cluster handles it' is not governance. it is the absence of governance.

  • 08

    Is it useful for managed Kubernetes (EKS, GKE, AKS)?

    Yes. Managed Kubernetes handles the control plane. it does NOT handle your workload architecture. Resource limits, security context, PDBs, probes, and NetworkPolicies are YOUR responsibility on every provider. The cloud provider manages etcd. You manage everything that runs on the nodes.

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