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Connect Agent DAG Scheduler 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. Determines the exact timing and order for complex agent workflows.

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 DAG Scheduler.

  1. 01

    Get task timing details

    Provides a deep dive into the temporal constraints of a specific task within a specific schedule

  2. 02

    Analyze workflow structure

    Validates the integrity of the provided task graph and determines the fundamental execution sequence

  3. 03

    Simulate execution schedule

    Calculates the actual timing and resource utilization of the workflow given a limited number of execution slots

Real work

What teams actually run

01

Multi-stage content generation

An agent needs to write a draft, then an image model needs to generate visuals, and finally, a human review agent needs to edit. This MCP schedules that sequence and predicts the total time.

02

Financial compliance auditing

A workflow requires checking transaction logs, running fraud detection, and generating a final report. The MCP ensures the steps run in the correct order and calculates the necessary time window.

03

Scientific simulation pipeline

Running a series of computational models where the output of Model A feeds into Model B. This MCP determines the minimum wall-clock time required for the entire simulation.

04

Software deployment testing

Testing requires running unit tests, integration tests, and performance benchmarks. The MCP schedules these steps, accounting for limited testing hardware.

One connector, every AI

Agent DAG 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 DAG 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

917ms average. Fast in production.

Agent DAG Scheduler is checked daily against the live service.

Daily averagePeak 1084ms
Oct 3Today
Fastest day
618ms
Slowest day
1084ms
14-day trend
Stable-2%

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 DAG Scheduler, so you can see the experience inside your AI.

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

Agent DAG Scheduler Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_O656AYX4WRWVNQ5H3XQd1rmgQGzNZ6Q3H91k9qor/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 DAG Scheduler capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "agent-dag-scheduler-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_O656AYX4WRWVNQ5H3XQd1rmgQGzNZ6Q3H91k9qor/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 DAG Scheduler in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Agent DAG Scheduler owners hand off.

This MCP is essential for anyone building complex, multi-step automated workflows. If your process involves multiple agents that must run in a specific, dependent order, this capability gives you the timing certainty you need. It moves your workflow from theoretical planning to predictable execution.

  • 01

    Workflow Architect

    Builds and validates the entire structure of agent interactions, ensuring no dead ends or circular dependencies exist.

  • 02

    Data Engineer

    Models data pipelines where the output of one step dictates the start time of the next, requiring precise timing calculations.

  • 03

    Project Manager

    Predicts the overall duration of a project phase, accounting for limited resources and parallel execution constraints.

FAQ

Questions Agent DAG Scheduler owners ask.

  • 01

    What is a DAG and why do I need this MCP?

    A DAG is a Directed Acyclic Graph, which is just a fancy way of saying a workflow with steps that have a clear, non-repeating order. You need this MCP because it calculates the timing and order for those steps, preventing your agents from running out of sequence or getting stuck in a loop.

  • 02

    Does this MCP handle resource limitations?

    Yes. The simulate_execution_schedule capability lets you input a limited number of parallel slots. It then calculates the actual wall-clock time and efficiency based on those constraints, giving you a realistic timeline.

  • 03

    What if my workflow has a cycle?

    If your workflow has a cycle (meaning Agent A depends on Agent B, and Agent B depends on Agent A), the analyze_workflow_structure capability will detect it and tell you the exact path of the cycle, allowing you to fix the logic.

  • 04

    Can I find out how flexible a task's timing is?

    The get_task_timing_details capability gives you a deep dive into specific tasks. It reports metrics like slack time, which tells you how much wiggle room that task has within the overall schedule.

  • 05

    How does the scheduler handle parallel execution?

    The simulate_execution_schedule capability calculates how tasks are distributed across a specified number of parallel slots, accounting for both dependency constraints and resource availability.

  • 06

    Can this capability detect errors in my workflow structure?

    Yes, by using analyze_workflow_structure, the engine validates if the task graph is a valid DAG and will return the specific cycle path if a loop is detected.

  • 07

    What is the difference between critical path and wall-clock time?

    The critical path is the longest sequence of dependent tasks, representing the absolute minimum time needed. Wall-clock time is the actual elapsed time when limited parallel slots are applied.

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