Agent DAG Scheduler Connector for AI agents.
3 live capabilities
Calculate deterministic execution orders for multi-agent workflows
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Why people use Agent DAG Scheduler
Stop agent loop errors with Agent DAG Scheduler workflow validation
With this MCP, that guesswork disappears. You feed the task requirements into the engine, and it instantly tells you if your logic is sound or if you've accidentally created a loop. It turns a messy, unpredictable process into a predictable, mathematically verified execution plan.
What Vinkius changes
Get the Agent DAG Scheduler answer you need in the AI client you already use.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,400+ Connectors
- Real-world use case 01
Fixing broken agent loops
An engineer builds a complex agent chain that keeps getting stuck in a loop.
- Real-world use case 02
Predicting project finish times
A developer needs to know if a 50-task workflow will take ten minutes or two hours.
- Real-world use case 03
Managing tight deadlines
A team is running a time-sensitive data pipeline.
Complete set · 3capabilities
The complete Agent DAG Scheduler capability set.
These are the exact actions your AI can choose when you ask it to work with Agent DAG Scheduler.
01—03
3 capabilities in this set.
Part of 3 available through Agent DAG Scheduler.
- 01 Capability
Analyze workflow structure
Checks your task graph for errors and determines the necessary execution sequence. It ensures your workflow is valid and free of infinite loops.
- 02 Capability
Get task timing details
Looks into the specific timing constraints of a single task. It helps you find the slack time available before a delay hits the main path.
- 03 Capability
Simulate execution schedule
Runs a simulation of your workflow using a set number of parallel slots. It tells you the actual wall-clock time and how efficient your resources are.
Set up in minutes
One URL. Then ask Agent DAG Scheduler to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Agent DAG 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_O656AYX4WRWVNQ5H3XQd1rmgQGzNZ6Q3H91k9qor/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 Agent DAG Scheduler, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Agent DAG Scheduler for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_O656AYX4WRWVNQ5H3XQd1rmgQGzNZ6Q3H91k9qor/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 Agent DAG Scheduler URL.
- Step 03
Save and start
Save the connection and enable Agent DAG Scheduler in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-dag-scheduler": {
"url": "https://edge.vinkius.com/vk_preview_O656AYX4WRWVNQ5H3XQd1rmgQGzNZ6Q3H91k9qor/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 Agent DAG Scheduler
Open Agent mode in chat and ask: "Using Agent DAG Scheduler, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-dag-scheduler": {
"url": "https://edge.vinkius.com/vk_preview_O656AYX4WRWVNQ5H3XQd1rmgQGzNZ6Q3H91k9qor/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 Agent DAG Scheduler
Ask Copilot: "Using Agent DAG Scheduler, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-dag-scheduler": {
"url": "https://edge.vinkius.com/vk_preview_O656AYX4WRWVNQ5H3XQd1rmgQGzNZ6Q3H91k9qor/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 Agent DAG Scheduler
Open Cascade and ask: "Using Agent DAG Scheduler, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-dag-scheduler": {
"url": "https://edge.vinkius.com/vk_preview_O656AYX4WRWVNQ5H3XQd1rmgQGzNZ6Q3H91k9qor/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 Agent DAG Scheduler
Ask Cline: "Using Agent DAG Scheduler, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add agent-dag-scheduler --transport http "https://edge.vinkius.com/vk_preview_O656AYX4WRWVNQ5H3XQd1rmgQGzNZ6Q3H91k9qor/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 Agent DAG Scheduler
Ask Claude: "Using Agent DAG Scheduler, show me...". 3 tools are ready
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Causal-Graph Navigator
LLMs reason by statistical proximity, confusing word co-occurrence with direct causal relationships. This capability forces causal graph isolation: identify entities as nodes, map directed influence edges, isolate statistical associations, validate graph coherence, and derive paths strictly from the DAG.
Incident Postmortem Prover
Most postmortems fail: vague timelines, symptom-level root causes, and action items with no owner. This capability forces SRE-grade rigor: minute-by-minute timeline reconstruction, systemic 5-Whys analysis, root cause isolation, accountable action items with owners and deadlines, and historical pattern detection.
Marilyn vos Savant Probabilistic Clarity Prover
Stop your AI from trusting its gut. force it to check intuition against actual probability before every conclusion.
Dual-Phase Skincare Timer
Calculates exact clock times for two-phase skincare steps.
Agent Timeout & Cascading Delay Calculator
Calculate deterministic timeout allocations and predict cascading delays in multi-agent workflows.
Agent Tool Selection Router
A deterministic engine for selecting and sequencing optimal capabilities for AI agents.
Bring your own AI
Change the model, client or framework. Keep Agent DAG 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 -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Agent DAG Scheduler.
The practical details behind the request, access and result.
How can Agent DAG Scheduler prevent my agents from getting stuck?
It uses graph validation to check for circular dependencies. It identifies if any tasks point back to an earlier task in a way that creates an infinite loop, allowing you to fix the logic before execution.
Can I use Agent DAG Scheduler to estimate project timelines?
Yes. You can simulate your workflow with a specific number of parallel execution slots to get a highly accurate estimate of the total wall-clock time required.
Does Agent DAG Scheduler work with any multi-agent setup?
It works with any setup where tasks can be represented as a Directed Acyclic Graph (DAG). If your tasks have clear dependencies, this MCP can model them.
How does Agent DAG Scheduler help with resource management?
By simulating execution with limited parallel slots, it shows you how much your tasks will overlap and how efficiently you are using your available processing capacity.
Can I find out which tasks are causing delays with Agent DAG Scheduler?
Yes. You can inspect specific tasks to find their slack time and identify the critical path, which consists of the tasks that directly dictate the total duration.
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.
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.
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.
One connection away
Give your agent a direct line to Agent DAG Scheduler.
Connect Agent DAG Scheduler once. Keep it beside 6,400+ managed Connectors when the next task needs more.
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