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Connect Metorial to ChatGPT, Claude and Gemini

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. Deploy, monitor, and scale serverless MCP logic instantly.

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 · 8 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 8 available through Metorial.

  1. 01

    Metorial delete server

    This capability dismantles logical parameters to remove a specific MCP instance.

  2. 02

    Metorial get server status

    Use this to check the health status and logical matrices of a hosted node.

  3. 03

    Metorial get usage metrics

    This capability aggregates cost boundaries and latency tracking data.

  4. 04

    Metorial invoke server tool

    This capability executes commands by routing them directly to a serverless container node.

Capability set02 / 02

05-08

4 capabilities in this set.

Part of 8 available through Metorial.

  1. 05

    Metorial list traces

    This capability polls transaction logs to find the limits of your MCP capability executions.

  2. 06

    Metorial deploy server

    Use this to trigger the structural provisioning of a new serverless MCP logic matrix.

  3. 07

    Metorial get trace details

    This capability provides a deep, linear dive into a specific execution interaction.

  4. 08

    Metorial list servers

    This capability enumerates every serverless MCP bound currently hosted in your Metorial workspace.

Real work

What teams actually run

01

Scaling Agentic Workflows

Provision new container instances as your agent's workload increases.

02

Debugging Agent Logic

Use granular traces to find exactly where a capability execution failed or slowed down.

03

Infrastructure Cost Control

Monitor usage metrics to keep serverless capability costs within defined boundaries.

04

Remote Capability Execution

Run complex capability schemas inside isolated, safe Metorial bounds.

Automate a task

Put Metorial 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

Metorial 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 Metorial 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

922ms average. Fast in production.

Metorial is checked daily against the live service.

Daily averagePeak 1148ms
Oct 3Today
Fastest day
798ms
Slowest day
1148ms
14-day trend
Slowing+29%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 8 capabilities arrive 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 Metorial, so you can see the experience inside your AI.

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

Metorial Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_u9gVDS60iTHUlTLh8tFcQxn710vU1GGlSnGwhKcw/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 — Metorial capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "metorial-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_u9gVDS60iTHUlTLh8tFcQxn710vU1GGlSnGwhKcw/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 Metorial in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Metorial owners hand off.

This MCP is designed for technical professionals managing the intersection of AI and cloud infrastructure.

  • 01

    AIOps Engineers

    They use this to track costs and evaluate latencies for agentic workflows.

  • 02

    Platform Engineers

    They use this to provision and manage the scaling of serverless MCP logic.

  • 03

    Systems Architects

    They use this to design the infrastructure that supports remote capability execution.

FAQ

Questions Metorial owners ask.

  • 01

    What is Metorial used for?

    Metorial is an infrastructure platform used to deploy, monitor, and scale serverless MCP logic and capabilities.

  • 02

    Can I monitor the cost of my AI capabilities?

    Yes, you can use the usage metrics capability to aggregate cost boundaries and track latency.

  • 03

    How does the agent handle capability deployment?

    The agent uses the deployment capability to trigger structural provisioning of remote serverless instances.

  • 04

    Is it safe to shut down capabilities?

    Yes, the decommissioning capability allows you to terminate idle endpoints without causing logic panics.

  • 05

    Which clients work with this MCP?

    This MCP works with any compatible client, including Claude, Cursor, and Windsurf.

  • 06

    Can I automatically deploy a new MCP logic container natively using Metorial?

    Yes! Utilize deploy_server explicit limits passing configurations to provision instances dynamically spinning up natively isolated.

  • 07

    Is it possible to track the detailed error bounds of a specific proxy execution?

    Yes! Interrogating the UUID via get_trace_details dumps end-to-end telemetry bounds explicitly isolating variables successfully.

  • 08

    Does the system aggregate LLM latency usage inherently?

    Exactly, call get_usage_metrics declaring explicitly bounding day limits to receive grouped logic matrices seamlessly.

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