MCP Task Orchestrator

Server-enforced workflow discipline for AI agents.

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

Task Orchestrator is an MCP server that gives AI coding agents a persistent work item graph with quality gates enforced by the server, not the prompt. It is built for developers running multi-agent or multi-session coding workflows: an orchestrator dispatching sub-agents, a fresh session picking up yesterday's work, or an autonomous loop draining a backlog. It ships as a Docker image, works with any MCP client, and has an optional Claude Code plugin that adds skills and hooks on top.

New here? Start with the illustrated field guide. It explains the ideas on this page in short visual pages, several of them interactive: fire triggers at a phase gate, click a work breakdown through its dependencies, and watch a schema resolve.

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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.

Task Orchestrator puts them in the server. If a required design note isn't filled, advance_item returns an error naming the missing note. If an upstream dependency isn't complete, the transition is blocked. Every transition and note records who made it. A new session recovers the full state in one call instead of replaying a conversation. And the rules are YAML config, not hardcoded prompts — change them without changing code.

What It Looks Like

Morning — new session, new agent, zero context:

Agent: get_context(since="2025-01-14T17:00:00Z")
       → 2 items in work, 1 blocked, 1 stalled (missing implementation-notes)
       → Recent transitions show orchestrator-1 dispatched 3 sub-agents yesterday
       → Full ancestor chains: "Auth Feature > Login API > Input validation"

Agent: advance_item(transitions=[{ itemId: "a3f2", trigger: "start",
         actor: { id: "morning-agent", kind: "subagent", parent: "orchestrator-1" } }])
       → Error: "Gate check failed: required notes not filled for queue phase: requirements"

Agent: manage_notes(operation="upsert", notes=[{ itemId: "a3f2", key: "requirements",
         body: "Validate email format, enforce password complexity...",
         actor: { id: "morning-agent", kind: "subagent" } }])
       → Upserted. noteProgress: { filled: 1, remaining: 0, total: 1 }

Agent: advance_item(transitions=[{ itemId: "a3f2", trigger: "start",
         actor: { id: "morning-agent", kind: "subagent" } }])
       → queue → work. Actor recorded. No context rebuilding.

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Quick Start

Prerequisite: Docker installed and running.

1. Register the server

The simplest setup is a per-session STDIO container: no port, no daemon, no REST API. Add it to your project's .mcp.json: