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task-orchestrator

MCP serverProductivity

task-orchestrator gives your AI a structured way to run multi-step projects, so work stays organized instead of scattered across conversations. It turns plans into work items, maps how tasks depend on each other, and enforces quality gates before anything counts as done. The result is steadier, more reliable progress on longer projects.

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What your AI can do with it

  • Break projects into trackable work items
  • Map task dependencies so steps happen in the right order
  • Enforce quality gates before work is marked complete
  • Keep multi-step projects organized from start to finish

Getting started

  1. Save this item in Your setup as a reference.
  2. Read the source or reference documentation for its setup requirements. Saving it here does not connect it to your AI.
  3. Check this page for availability before trying to install it through Ahel.

From the project's README

As published by jpicklyk/task-orchestrator in README.md. This repository publishes several servers, so only the opening is shown here.

Server-enforced workflow discipline for AI agents.

Prompt-based frameworks hope the LLM follows instructions. This one blocks the call if it doesn't.

The Problem

Multi-agent workflows need infrastructure the model doesn't provide. When an orchestrator dispatches sub-agents across sessions, there's no built-in way to enforce what documentation must exist before work starts, track which agent made which change, or guarantee dependency ordering across a work breakdown. These are structural concerns — they belong in the server, not in prompts.

A Different Approach

Shortened here. Read the whole README on GitHub.

Signals

GitHub stars
207
Forks
24
Last commit
Sep 2026
Advanced
Delivery
task-orchestrator MCP server → your Ahel connector (mcp.ahel.ai) → your AI.
Item type
mcp-server
Key
io-github-jpicklyk-task-orchestrator
Source
github.com/jpicklyk/task-orchestrator

github.com/jpicklyk/task-orchestrator

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