DAG Topological Scheduler Connector for AI agents.
3 live capabilities
Optimize multi-agent task execution and critical path scheduling
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Why people use DAG Topological Scheduler
Solve multi-agent dependency chaos with DAG Topological Scheduler
This MCP changes that by bringing mathematical rigor to your agent's planning phase. Instead of reacting to failures, your agent uses the scheduler to build a perfect execution map. You get a clear, predictable timeline that accounts for every dependency and resource constraint.
What Vinkius changes
You get a mathematically guaranteed execution plan for complex agent workflows.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,400+ Connectors
- Real-world use case 01
Scaling multi-agent research swarms
An engineer needs to run 50 research tasks.
- Real-world use case 02
Automated software build pipelines
A developer uses the scheduler to manage complex code compilation and testing steps that must happen in a specific, non-linear order.
- Real-world use case 03
Complex data processing workflows
A data scientist uses the capability to map out a massive ETL pipeline, ensuring that data cleaning happens before analysis without manual oversight.
Complete set · 3capabilities
The complete DAG Topological Scheduler capability set.
These are the exact actions your AI can choose when you ask it to work with DAG Topological Scheduler.
01—03
3 capabilities in this set.
Part of 3 available through DAG Topological Scheduler.
- 01 Capability
Analyze dag structure
Validates that your task graph is logically sound and identifies the core sequence. It catches errors like circular dependencies before they break your run.
- 02 Capability
Calculate slack and bottlenecks
Finds which tasks are flexible and which ones are rigid. It highlights the specific points where a delay will stall your entire project.
- 03 Capability
Simulate agent schedule
Predicts the total time to completion for a given number of agents. It helps you decide if you need more workers to hit a deadline.
Set up in minutes
One URL. Then ask DAG Topological Scheduler to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use DAG Topological Scheduler from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_XVNVc4tTweXCWVCyHW44A1epxFqNSFXWXTiGbnEP/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it DAG Topological Scheduler, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable DAG Topological Scheduler for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_XVNVc4tTweXCWVCyHW44A1epxFqNSFXWXTiGbnEP/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the DAG Topological Scheduler URL.
- Step 03
Save and start
Save the connection and enable DAG Topological Scheduler in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"dag-topological-scheduler": {
"url": "https://edge.vinkius.com/vk_preview_XVNVc4tTweXCWVCyHW44A1epxFqNSFXWXTiGbnEP/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using DAG Topological Scheduler
Open Agent mode in chat and ask: "Using DAG Topological Scheduler, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"dag-topological-scheduler": {
"url": "https://edge.vinkius.com/vk_preview_XVNVc4tTweXCWVCyHW44A1epxFqNSFXWXTiGbnEP/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using DAG Topological Scheduler
Ask Copilot: "Using DAG Topological Scheduler, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"dag-topological-scheduler": {
"url": "https://edge.vinkius.com/vk_preview_XVNVc4tTweXCWVCyHW44A1epxFqNSFXWXTiGbnEP/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using DAG Topological Scheduler
Open Cascade and ask: "Using DAG Topological Scheduler, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"dag-topological-scheduler": {
"url": "https://edge.vinkius.com/vk_preview_XVNVc4tTweXCWVCyHW44A1epxFqNSFXWXTiGbnEP/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using DAG Topological Scheduler
Ask Cline: "Using DAG Topological Scheduler, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add dag-topological-scheduler --transport http "https://edge.vinkius.com/vk_preview_XVNVc4tTweXCWVCyHW44A1epxFqNSFXWXTiGbnEP/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using DAG Topological Scheduler
Ask Claude: "Using DAG Topological Scheduler, show me...". 3 tools are ready
Where the request belongs
Work DAG Topological Scheduler can move forward.
This is for engineers and researchers building autonomous agent swarms who are tired of unpredictable execution times and dependency errors.
AI Orchestration Engineer
Designing multi-agent systems that require strict dependency management and resource optimization.
Workflow Automation Developer
Building complex, non-linear automation pipelines that need to scale across multiple workers.
Operations Researcher
Simulating complex task scheduling problems to find the most efficient way to deploy compute resources.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Agent Parallel Execution Optimizer
Optimize task distribution and efficiency metrics for agent swarms.
Load Balancer Distributor
Deterministic simulation engine for evaluating load balancing algorithms.
Priority Queue with Aging Scheduler
A deterministic scheduler that manages task execution using priority-based queues with an anti-starvation aging mechanism.
Agent Resource Fairness Scheduler
Deterministic fair resource allocation for competing agents using weighted fair queuing.
Priority Inversion Detector
Detect and resolve priority inversion in real-time systems.
Agent Task Decomposition Engine
Break complex goals into structured subtasks with deterministic complexity scoring.
Bring your own AI
Change the model, client or framework. Keep DAG Topological Scheduler connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
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Amazon Q -
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Jan -
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Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
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LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about DAG Topological Scheduler.
The practical details behind the request, access and result.
How can the DAG Topological Scheduler help my AI agents work faster?
It allows your agents to identify which tasks can be run simultaneously and which ones are blocking progress, ensuring they don't waste time waiting on unnecessary dependencies.
Can I use DAG Topological Scheduler to save money on agent costs?
Yes. By simulating different numbers of agents, you can find the 'sweet spot' where you have enough workers to be fast without paying for idle agents that are just waiting on a bottleneck.
Will DAG Topological Scheduler prevent my agent workflows from crashing?
It helps prevent crashes caused by circular dependencies. It validates the logic of your task graph before your agents start executing, catching errors early.
Does DAG Topological Scheduler work with any agent framework?
Yes, as long as your agent client is MCP-compatible, like Claude, Cursor, or Windsurf, it can use these scheduling capabilities to manage its tasks.
How does the critical path feature work in DAG Topological Scheduler?
It identifies the longest chain of dependent tasks. This tells you exactly which tasks must be completed on time to prevent the entire project from being delayed.
One connection away
Give your agent a direct line to DAG Topological Scheduler.
Connect DAG Topological Scheduler once. Keep it beside 6,400+ managed Connectors when the next task needs more.
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