Task DAG Dependency Resolver Connector for AI agents.
3 live capabilities
Calculate valid task execution orders and parallel groups
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Why people use Task DAG Dependency Resolver
Solving pipeline deadlocks with Task DAG Dependency Resolver
This MCP changes that by treating your tasks as a mathematical graph. Instead of guessing, you get a clear report on exactly where the loop is. It turns a multi-hour debugging session into a five-second query.
What Vinkius changes
You get a mathematically verified roadmap for your task execution.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Fixing stuck agent workflows
An engineer's multi-agent loop is stuck.
- Real-world use case 02
Optimizing parallel execution
A developer wants to speed up a long-running pipeline.
- Real-world use case 03
Cleaning up orphaned tasks
A researcher notices some tasks never run.
Complete set · 3capabilities
The complete Task DAG Dependency Resolver capability set.
These are the exact actions your AI can choose when you ask it to work with Task DAG Dependency Resolver.
01—03
3 capabilities in this set.
Part of 3 available through Task DAG Dependency Resolver.
- 01 Capability
Detect cycles
Finds and describes the exact loop causing a deadlock in your pipeline. It pinpoints the specific tasks creating a circular dependency.
- 02 Capability
Find isolated tasks
Identifies tasks that are completely disconnected from the rest of the execution graph. This helps you find orphaned tasks that won't run.
- 03 Capability
Resolve execution order
Calculates the valid execution sequence and parallel groups for a set of tasks. It provides the optimal path for running your workflow.
Set up in minutes
One URL. Then ask Task DAG Dependency Resolver to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Task DAG Dependency Resolver 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_PG7vL55vQOWAb30PXDjIN58o0NkWX6bQwzxPi4mp/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 Task DAG Dependency Resolver, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Task DAG Dependency Resolver for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_PG7vL55vQOWAb30PXDjIN58o0NkWX6bQwzxPi4mp/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 Task DAG Dependency Resolver URL.
- Step 03
Save and start
Save the connection and enable Task DAG Dependency Resolver in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"task-dag-dependency-resolver": {
"url": "https://edge.vinkius.com/vk_preview_PG7vL55vQOWAb30PXDjIN58o0NkWX6bQwzxPi4mp/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 Task DAG Dependency Resolver
Open Agent mode in chat and ask: "Using Task DAG Dependency Resolver, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"task-dag-dependency-resolver": {
"url": "https://edge.vinkius.com/vk_preview_PG7vL55vQOWAb30PXDjIN58o0NkWX6bQwzxPi4mp/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 Task DAG Dependency Resolver
Ask Copilot: "Using Task DAG Dependency Resolver, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"task-dag-dependency-resolver": {
"url": "https://edge.vinkius.com/vk_preview_PG7vL55vQOWAb30PXDjIN58o0NkWX6bQwzxPi4mp/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 Task DAG Dependency Resolver
Open Cascade and ask: "Using Task DAG Dependency Resolver, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"task-dag-dependency-resolver": {
"url": "https://edge.vinkius.com/vk_preview_PG7vL55vQOWAb30PXDjIN58o0NkWX6bQwzxPi4mp/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 Task DAG Dependency Resolver
Ask Cline: "Using Task DAG Dependency Resolver, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add task-dag-dependency-resolver --transport http "https://edge.vinkius.com/vk_preview_PG7vL55vQOWAb30PXDjIN58o0NkWX6bQwzxPi4mp/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 Task DAG Dependency Resolver
Ask Claude: "Using Task DAG Dependency Resolver, show me...". 3 tools are ready
Where the request belongs
Work Task DAG Dependency Resolver can move forward.
This is for engineers and researchers building complex, multi-step agentic workflows that rely on strict task ordering.
AI Engineer
Designing multi-agent systems that need to coordinate complex, interdependent operations.
Workflow Architect
Building automated pipelines where task order and parallelization are critical for performance.
Data Engineer
Managing complex DAGs in data processing pipelines to ensure reliable execution.
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Bring your own AI
Change the model, client or framework. Keep Task DAG Dependency Resolver connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
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Vercel AI SDK
Before you connect
Questions about Task DAG Dependency Resolver.
The practical details behind the request, access and result.
How can the Task DAG Dependency Resolver help my agent workflows?
It provides the mathematical logic needed to organize complex task sequences, find parallel execution opportunities, and catch deadlocks before they crash your system.
Can I use this MCP to speed up my task execution?
Yes. By identifying which tasks have no mutual dependencies, it allows you to run them in parallel groups rather than one by one.
Will this MCP find errors in my task logic?
It specifically finds circular dependencies that cause deadlocks and identifies tasks that are disconnected from your main workflow.
Is this MCP compatible with CrewAI or LangGraph?
Yes, it is designed to work with any orchestration framework that manages task dependencies through directed acyclic graphs.
How does the Task DAG Dependency Resolver handle deadlocks?
It uses graph analysis to pinpoint the exact set of tasks forming a loop, making it easy to see where your logic is stuck.
How do I find tasks that can run at the same time?
You can use the resolve_execution_order capability, which returns parallelGroups containing sets of task IDs that share the same depth level in the dependency graph.
What happens if my task list has a circular dependency?
If a cycle is detected, you can use detect_cycles to retrieve the exact cyclePath that forms the loop, allowing you to fix the deadlock.
Can I use this for LangGraph or CrewAI workflows?
Yes, this server is specifically built to resolve execution orders for agentic pipelines like those used in LangGraph and CrewAI.
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