Orchestrator Server
What is Orchestrator Server
The orchestrator-server is a small MCP server designed for managing and coordinating tasks across multiple LLM instances, such as Claude Desktop or Cline. It enables AI agents to create, share, and execute tasks seamlessly across different instances.
Use cases
Use cases for the orchestrator-server include managing workflows across multiple AI instances, automating task execution in AI applications, and ensuring efficient task coordination in complex systems involving LLMs.
How to use
To use the orchestrator-server, install it via npm with ‘npm install’, then build it using ‘npm run build’. You can create tasks, retrieve the next task for an instance, and mark tasks as complete using the provided API methods.
Key features
Key features include task creation with dependencies, multi-instance coordination, persistent task storage, dependency enforcement, task status tracking, task updates, safe deletion with dependency checks, cycle detection, and enhanced state management.
Where to use
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Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Orchestrator Server
The orchestrator-server is a small MCP server designed for managing and coordinating tasks across multiple LLM instances, such as Claude Desktop or Cline. It enables AI agents to create, share, and execute tasks seamlessly across different instances.
Use cases
Use cases for the orchestrator-server include managing workflows across multiple AI instances, automating task execution in AI applications, and ensuring efficient task coordination in complex systems involving LLMs.
How to use
To use the orchestrator-server, install it via npm with ‘npm install’, then build it using ‘npm run build’. You can create tasks, retrieve the next task for an instance, and mark tasks as complete using the provided API methods.
Key features
Key features include task creation with dependencies, multi-instance coordination, persistent task storage, dependency enforcement, task status tracking, task updates, safe deletion with dependency checks, cycle detection, and enhanced state management.
Where to use
undefined
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
MCP Orchestrator Server
The MCP Orchestrator Server provides task management and coordination capabilities across MCP enabled LLM instances like Claude Desktop or Cline. In simpler terms it allows for AI agents to create, share and execute tasks across instances
Features
Version 1.1.0
- Task Updates: Modify pending tasks
- Safe Deletion: Delete tasks with dependency checks
- Cycle Detection: Prevent dependency cycles
- Tool Listing: Comprehensive tool documentation
- Enhanced State Management: Improved task state transitions
Core Features
- Task creation with dependencies
- Multi-instance coordination
- Persistent task storage
- Dependency enforcement
- Task status tracking
Installation
Installing via Smithery
To install Orchestrator Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install orchestrator-server --client claude
Manual Installation
npm install npm run build
Usage
Create a Task
await create_task({
id: 'setup',
description: 'Initial setup'
});
Get Next Task
const task = await get_next_task({
instance_id: 'worker-1'
});
Complete Task
await complete_task({
task_id: 'setup',
instance_id: 'worker-1',
result: 'System initialized'
});
Documentation
Roadmap
Version 1.2.0
- Task priorities
- Timeouts
- Instance management
Version 1.3.0
- Task groups
- Analytics
- Dashboard
License
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
