What is a DAG in the context of this MCP?

A Directed Acyclic Graph is a collection of tasks where each task has one or more dependencies, and there are no loops or cycles.

How does this MCP help with multi-agent systems?

It provides the math to decide which agent should do which task and when, based on the dependencies and available resources.

Can I use this with Cursor or Windsurf?

Yes, this MCP is compatible with any MCP-compatible client including Cursor and Windsurf.

What is a 'makespan'?

Makespan is the total time elapsed from the start of the first task to the completion of the last task in a schedule.

Does this MCP host the data?

Vinkius hosts the MCP and manages the execution, so you only need to connect your client once to start using the capabilities.

How does the scheduler handle parallel execution?

The server uses list scheduling to assign ready tasks to available agents, prioritizing tasks on the critical path to minimize total makespan.

Can it detect errors in my task graph?

Yes, by using analyze_dag_structure, the engine can detect if a graph contains cycles, which would make it an invalid DAG.

What is a bottleneck task?

A bottleneck task is a task with zero slack time. Any delay in these tasks will directly delay the entire project completion.

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Connect DAG Topological Scheduler 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. Get deterministic execution orders and critical path analysis for multi-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.

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.

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 DAG Topological Scheduler.

01
Analyze dag structure
This capability validates your task graph to ensure it is structurally sound and identifies the core sequence and critical path.
02
Calculate slack and bottlenecks
Use this to find task flexibility and pinpoint exactly which tasks are causing delays in your workflow.
03
Simulate agent schedule
This capability calculates the actual execution timeline and efficiency based on a specific number of available agents.

Real work

What teams actually run

Multi-Agent Orchestration

Coordinate a swarm of agents working on a complex software engineering project with interdependent tasks.

Automated Pipeline Validation

Check that a newly generated set of CI/CD tasks does not contain circular dependencies.

Resource Planning

Determine if adding a third agent will actually speed up a specific set of tasks or if the bottleneck is elsewhere.

Project Timeline Modeling

Calculate the minimum time required to complete a batch of data processing jobs.

One connector, every AI

DAG Topological 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.

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

You don't need a client to put DAG Topological 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

840ms average. Fast in production.

DAG Topological Scheduler is checked daily against the live service.

Daily averagePeak 1089ms

Oct 3Today

Fastest day: 714ms
Slowest day: 1089ms
14-day trend: Improving-7%

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

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

DAG Topological Scheduler Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_XVNVc4tTweXCWVCyHW44A1epxFqNSFXWXTiGbnEP/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — DAG Topological Scheduler capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "dag-topological-scheduler-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_XVNVc4tTweXCWVCyHW44A1epxFqNSFXWXTiGbnEP/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 DAG Topological Scheduler in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work DAG Topological Scheduler owners hand off.

This MCP is built for developers and engineers building complex, multi-agent autonomous systems.

01
AI Engineers
They hand off complex agent orchestration logic to the MCP to ensure deterministic execution.
02
Workflow Developers
They use the capability to validate task dependencies and optimize automated pipelines.
03
Systems Architects
They use the simulation capabilities to model resource requirements for large scale agent deployments.

FAQ

Questions DAG Topological Scheduler owners ask.

01
What is a DAG in the context of this MCP?
A Directed Acyclic Graph is a collection of tasks where each task has one or more dependencies, and there are no loops or cycles.
02
How does this MCP help with multi-agent systems?
It provides the math to decide which agent should do which task and when, based on the dependencies and available resources.
03
Can I use this with Cursor or Windsurf?
Yes, this MCP is compatible with any MCP-compatible client including Cursor and Windsurf.
04
What is a 'makespan'?
Makespan is the total time elapsed from the start of the first task to the completion of the last task in a schedule.
05
Does this MCP host the data?
Vinkius hosts the MCP and manages the execution, so you only need to connect your client once to start using the capabilities.
06
How does the scheduler handle parallel execution?
The server uses list scheduling to assign ready tasks to available agents, prioritizing tasks on the critical path to minimize total makespan.
07
Can it detect errors in my task graph?
Yes, by using analyze_dag_structure, the engine can detect if a graph contains cycles, which would make it an invalid DAG.
08
What is a bottleneck task?
A bottleneck task is a task with zero slack time. Any delay in these tasks will directly delay the entire project completion.

Explore

More in Scheduling

Connect DAG Topological Scheduler to Claude, Cursor, ChatGPT & more

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

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Analyze dag structure

Calculate slack and bottlenecks

Simulate agent schedule

What teams actually run

Multi-Agent Orchestration

Automated Pipeline Validation

Resource Planning

Project Timeline Modeling

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

DAG Topological Scheduler Connector

Claude Desktop

AI Engineers

Workflow Developers

Systems Architects

What is a DAG in the context of this MCP?

How does this MCP help with multi-agent systems?

Can I use this with Cursor or Windsurf?

What is a 'makespan'?

Does this MCP host the data?

How does the scheduler handle parallel execution?

Can it detect errors in my task graph?

What is a bottleneck task?

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