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Connect Agent Resource Fairness Scheduler to your AI

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. Keep all your competing agents running fairly and predictably.

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 · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Agent Resource Fairness Scheduler.

  1. 01

    Calculate resource allocation

    Determine exact token and compute distribution for all agents based on priorities and requests

  2. 02

    Estimate queue metrics

    Predict future system behavior, including queue drain time and preemption costs

  3. 03

    Analyze system health

    Evaluate system state regarding fairness, starvation, and priority issues

Real work

What teams actually run

01

High-Volume Chatbots

When hundreds of users hit your chatbot simultaneously, this MCP ensures that no single user's request starves the entire system.

02

Financial Trading Agents

If multiple agents are making trades and competing for compute, this MCP guarantees that all agents get their required processing time.

03

Automated Data Processing Pipelines

Use it when a pipeline has several sequential, resource-intensive steps, ensuring each step gets its fair share of tokens.

04

Multi-Service Orchestration

For complex systems where different services (e.g., search, generation, summarization) run concurrently, this maintains stability.

One connector, every AI

Agent Resource Fairness Scheduler 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 Agent Resource Fairness Scheduler 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

878ms average. Fast in production.

Agent Resource Fairness Scheduler is checked daily against the live service.

Daily averagePeak 1042ms
Oct 3Today
Fastest day
684ms
Slowest day
1042ms
14-day trend
Stable+3%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 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 Agent Resource Fairness Scheduler, so you can see the experience inside your AI.

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

Agent Resource Fairness Scheduler Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_N4XhWdH8YrrAB6beib6DSdaMQ1hBgnOVd2fR3toU/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 — Agent Resource Fairness Scheduler capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "agent-resource-fairness-scheduler-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_N4XhWdH8YrrAB6beib6DSdaMQ1hBgnOVd2fR3toU/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 Agent Resource Fairness Scheduler in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Agent Resource Fairness Scheduler owners hand off.

This MCP is essential for developers building complex, multi-agent applications. If your system relies on many independent AI agents running concurrently, you need predictable resource management. It gives you the capabilities to guarantee that no single agent can monopolize the compute power.

  • 01

    MLOps Engineer

    Use this to monitor and guarantee that resource allocation remains stable under heavy load.

  • 02

    AI Architect

    Use this to design multi-agent workflows that require deterministic and fair resource sharing.

  • 03

    Software Developer

    Use this to build robust applications that won't fail or degrade when multiple services run simultaneously.

FAQ

Questions Agent Resource Fairness Scheduler owners ask.

  • 01

    What is Weighted Fair Queuing (WFQ)?

    WFQ is a resource management technique that ensures bandwidth is distributed fairly among multiple users or agents. It guarantees that each agent gets a share of resources based on its specific weight or priority, preventing any single agent from monopolizing the system.

  • 02

    Does this MCP handle token limits?

    Yes. It is designed to manage both token and compute capacity. You can use the calculate_resource_allocation capability to determine exactly how many tokens each agent receives based on its priority and the total available pool.

  • 03

    What is Jain's Fairness Index?

    It's a mathematical metric used by the MCP to measure how evenly resources are distributed. A score of 1.0 means perfect, absolute fairness across all competing agents.

  • 04

    Can I predict system slowdowns?

    Absolutely. The estimate_queue_metrics capability lets you predict future behavior, giving you metrics like queue drain time and preemption costs so you can plan for bottlenecks.

  • 05

    Is this MCP only for tokens?

    No. It manages both token capacity and compute capacity (measured in milliseconds). It treats them as two separate, limited resources that must be allocated fairly.

  • 06

    How is resource allocation calculated?

    Resources are distributed using Weighted Fair Queuing (WFQ). An agent's share is calculated as its priority divided by the sum of all active agents' priorities, multiplied by the total available capacity. You can use calculate_resource_allocation to get exact distributions.

  • 07

    How does the system detect starvation?

    Starvation is flagged if an agent's waiting time exceeds five times the average wait time of all agents in the queue. The analyze_system_health capability will report this in the health status.

  • 08

    Can agents use more than their fair share?

    Yes, through Burst Allocation. If other agents are not utilizing their full share, an agent can borrow from the idle pool up to a maximum of twice its own calculated fair share.

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