Parallel Task Fan-out Coordinator Connector for AI agents.
3 live capabilities
Synchronize parallel agentic workflows with deterministic coordination strategies.
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Why people use Parallel Task Fan-out Coordinator
Solve agent synchronization issues with Parallel Task Fan-out Coordinator
This MCP acts as those traffic lights. By grouping tasks into batches, you can enforce rules that dictate exactly when a group is 'done.' You can wait for everyone, wait for a majority, or move as soon as the first one succeeds. It turns a messy pile of asynchronous events into a predictable, structured pipeline.
What Vinkius changes
This MCP provides the structural guardrails needed to turn independent agent tasks into a synchronized, multi-stage workflow.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Consensus-based fact checking
An agent sends a claim to three different search agents.
- Real-world use case 02
Parallel code review
A codebase is split into modules, and multiple agents review them simultaneously.
- Real-world use case 03
Multi-perspective research
An agent triggers a fan-out of specialized research agents.
Complete set · 3capabilities
The complete Parallel Task Fan-out Coordinator capability set.
These are the exact actions your AI can choose when you ask it to work with Parallel Task Fan-out Coordinator.
01—03
3 capabilities in this set.
Part of 3 available through Parallel Task Fan-out Coordinator.
- 01 Capability
Get batch summary
Fetches the current state and results for an entire batch of tasks. It tells you if the group has met its threshold to proceed.
- 02 Capability
Register task batch
Creates a new group of tasks and assigns a specific coordination strategy like barrier or quorum. This sets the rules for how the group will eventually sync.
- 03 Capability
Update task status
Sends the completion status and resulting data from an individual task to the coordinator. This keeps the group's progress updated in real time.
Set up in minutes
One URL. Then ask Parallel Task Fan-out Coordinator to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Parallel Task Fan-out Coordinator 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_elCb8wpPopd75uCL9DwkRf5ffmt3go594goWwsik/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 Parallel Task Fan-out Coordinator, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Parallel Task Fan-out Coordinator for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_elCb8wpPopd75uCL9DwkRf5ffmt3go594goWwsik/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 Parallel Task Fan-out Coordinator URL.
- Step 03
Save and start
Save the connection and enable Parallel Task Fan-out Coordinator in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"parallel-task-fan-out-coordinator": {
"url": "https://edge.vinkius.com/vk_preview_elCb8wpPopd75uCL9DwkRf5ffmt3go594goWwsik/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 Parallel Task Fan-out Coordinator
Open Agent mode in chat and ask: "Using Parallel Task Fan-out Coordinator, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"parallel-task-fan-out-coordinator": {
"url": "https://edge.vinkius.com/vk_preview_elCb8wpPopd75uCL9DwkRf5ffmt3go594goWwsik/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 Parallel Task Fan-out Coordinator
Ask Copilot: "Using Parallel Task Fan-out Coordinator, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"parallel-task-fan-out-coordinator": {
"url": "https://edge.vinkius.com/vk_preview_elCb8wpPopd75uCL9DwkRf5ffmt3go594goWwsik/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 Parallel Task Fan-out Coordinator
Open Cascade and ask: "Using Parallel Task Fan-out Coordinator, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"parallel-task-fan-out-coordinator": {
"url": "https://edge.vinkius.com/vk_preview_elCb8wpPopd75uCL9DwkRf5ffmt3go594goWwsik/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 Parallel Task Fan-out Coordinator
Ask Cline: "Using Parallel Task Fan-out Coordinator, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add parallel-task-fan-out-coordinator --transport http "https://edge.vinkius.com/vk_preview_elCb8wpPopd75uCL9DwkRf5ffmt3go594goWwsik/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 Parallel Task Fan-out Coordinator
Ask Claude: "Using Parallel Task Fan-out Coordinator, show me...". 3 tools are ready
Where the request belongs
Work Parallel Task Fan-out Coordinator can move forward.
This is for engineers and researchers building complex, multi-agent systems where timing and consensus are critical to the final output.
AI Orchestration Engineer
Building multi-agent frameworks that require strict synchronization between parallel reasoning steps.
LLM Developer
Designing complex agentic loops that rely on quorum-based decision making or consensus.
Workflow Automation Architect
Scaling agentic workflows that involve massive fan-out/fan-in patterns across distributed tasks.
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Bring your own AI
Change the model, client or framework. Keep Parallel Task Fan-out Coordinator connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
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Vercel AI SDK
Before you connect
Questions about Parallel Task Fan-out Coordinator.
The practical details behind the request, access and result.
How can I use Parallel Task Fan-out Coordinator to manage multiple agents?
You can group your agents into batches and assign them a strategy, like requiring a majority to agree before the workflow continues. This keeps them from running out of sync.
Does Parallel Task Fan-out Coordinator support quorum-based voting?
Yes, you can set a quorum requirement for any batch of tasks, ensuring the workflow only moves forward once a specific number of agents have successfully completed their work.
Can I use Parallel Task Fan-out Coordinator for simple sequential tasks?
It's better to use a standard sequential workflow for simple tasks. This MCP is specifically designed for parallel tasks that need to be synchronized or aggregated at a specific point.
How do I know when my parallel agent tasks are finished?
You can check the summary of your task group at any time to see how many tasks have finished and whether the group has met its required threshold to proceed.
Is it possible to wait for all agents to finish before moving to the next step?
Yes, you can use a barrier strategy which ensures the entire group of tasks must reach completion before the coordination is considered finished.
What synchronization strategies are supported?
The coordinator supports three strategies: barrier (waits for all tasks), first_success (proceeds after one success), and quorum (proceeds after a specific number of successes).
How do I check if my parallel tasks are finished?
You can use get_batch_summary to retrieve the current state of the batch, including whether the synchronization threshold has been met and the results of completed tasks.
Can I set a timeout for a batch of tasks?
Yes, when calling register_task_batch, you can provide a maxWaitMs parameter to enforce a deadline for the entire batch.
One connection away
Give your agent a direct line to Parallel Task Fan-out Coordinator.
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