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.

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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.

A Different Approach

Task Orchestrator is an MCP server — not a prompt layer. It provides 13 tools that give any MCP-compatible AI agent a persistent work item graph with server-enforced quality gates. The enforcement happens at the tool level: if a required design note isn't filled, advance_item returns an error. If a dependency isn't satisfied, the transition is blocked. If actor authentication is enabled and an agent doesn't identify itself, the call is rejected before it reaches the server.

The rules live in the server, not the conversation.

What this means in practice:

• An agent can't start implementation without filling the required specification note
• A sub-agent can't advance a blocked task until its upstream dependency is complete
• Every transition and note records who made the change (actor attribution)
• Auditing mode blocks any write operation where the agent doesn't identify itself
• A new session picks up exactly where the last one left off — persistent state, not conversation replay
• Workflow schemas are YAML config, not hardcoded prompts — change the rules without changing code

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How It's Different

	Prompt-Based Frameworks	Task Orchestrator
Enforcement	Instructions that agents should follow	Server blocks the call if rules aren't met
Persistence	File-based state	SQLite database with structured queries
Accountability	No concept of which agent did what	Actor attribution with pluggable verification (JWKS)
Dependency ordering	Sequenced by prompt convention	Server validates dependency graphs before allowing transitions
Session continuity	Conversation history or file reconstruction	get_context() returns full state in one call
Portability	Tied to one AI client	Works with any MCP-compatible client

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Core Capabilities

Workflow Enforcement

Schemas define what agents must produce at each phase — and the server blocks progression until it's done. But schemas do more than gate transitions. They set a planning floor: when an agent enters plan mode, the schema tells it what documentation must exist before implementation can start, shaping the plan structure itself.

# .taskorchestrator/config.yaml
work_item_schemas:
  feature-task:
    notes:
      - key: requirements
        role: queue
        required: true
        description: "Acceptance criteria before starting"
        guidance: "Cover: problem statement, acceptance criteria, alternatives considered, test strategy."
        skill: "spec-quality"
      - key: implementation-notes
        role: work
        required: true
        description: "What was built and why"

advance_item(trigger="start") from queue requires requirements to be filled. No exceptions, no prompt-dependent compliance — the server returns an error with exactly which notes are missing.

The guidance field provides authoring instructions surfaced at the right moment — when the agent is about to fill that note, get_context returns the guidance as a guidancePointer. The skill field takes this further: it references a specific skill that the agent must invoke before filling the note, providing a deterministic evaluation framework rather than freeform prose. Together, they create structured agent behavior that's configured in YAML, not hardcoded in prompts.

Composable Traits

Traits add cross-cutting note requirements to any schema without duplicating definitions. Define a trait once, apply it to any item type:

traits:
  needs-security-review:
    notes:
      - key: security-assessment
        role: review
        required: true
        description: "Security review of auth, data handling, and access control"
        skill: "security-review"

work_item_schemas:
  feature-task:
    default_traits:
      - needs-security-review
    notes:
      # ... base notes

Every feature-task item automatically inherits the security-assessment note requirement. Traits can also be applied per-item via the traits parameter on manage_items — a task touching authentication gets needs-security-review while a CSS cleanup doesn't.

Persistent Work Item Graph

Everything is a WorkItem in a hierarchical graph. Items nest up to 4 levels deep, connected by typed dependency edges. Create an entire work breakdown atomically:

create_work_tree(
  root={ "title": "User Authentication" },
  children=[
    { "ref": "schema", "title": "Database schema" },
    { "ref": "api",    "title": "Login API" },
    { "ref": "tests",  "title": "Integration tests" }
  ],
  deps=[
    { "from": "schema", "to": "api" },
    { "from": "api",    "to": "tests" }
  ]
)

When schema reaches terminal, api is automatically unblocked. When all children complete, the parent cascades to terminal. Dependency ordering is enforced by the server — structurally, not by convention.

Actor Attribution & Auditing

Every advance_item transition and manage_notes upsert accepts an optional actor claim:

{
  "actor": {
    "id": "impl-agent-42",
    "kind": "subagent",
    "parent": "orchestrator-1"
  }
}

Enable actor authentication in config to require it:

actor_authentication:
  enabled: true

When enabled, calls without actor claims are blocked before reaching the server. Query responses include the full delegation chain — which orchestrator dispatched which sub-agent, who wrote which note, who made which transition. Post-mortem debugging becomes a data query, not a conversation archaeology exercise.

Session Continuity

No context rebuilding. One call recovers the full picture:

get_context(since="2025-01-15T09:00:00Z", includeAncestors=true)

Returns active items, recent transitions (with actor attribution), blocked items, stalled items with missing notes, and full ancestor chains. A new session has complete state in a single response.

Notes as Structured Context

Notes provide targeted, phase-specific documentation attached to work items. An implementation agent reads a concise requirements note scoped to its task rather than scanning broader project context.

Notes are keyed, role-scoped, and queryable:

query_notes(itemId="<uuid>", role="work", includeBody=false)

Metadata-only queries (includeBody=false) let agents check what exists without paying the token cost of reading every note body.

Design Philosophy

Task Orchestrator enforces workflow structure without imposing methodology. The server owns the guardrails — role transitions, dependency ordering, gate enforcement, and accountability. Agents own everything else. There are no mandatory planning ceremonies, no prescribed development processes, no opinion on how agents approach implementation. Schemas, traits, and actor authentication are opt-in layers that integrate with your team's development policies through .taskorchestrator/config.yaml. As models gain new capabilities, the harness stays out of the way rather than constraining what agents can do.

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Quick Start

Prerequisite: Docker installed and running.

1. Pull the image

docker pull ghcr.io/jpicklyk/task-orchestrator:latest

2. Register with your MCP client

Claude Code (recommended):

claude mcp add-json mcp-task-orchestrator '{
  "command": "docker",
  "args": [
    "run", "--rm", "-i",
    "-v", "mcp-task-data:/app/data",
    "ghcr.io/jpicklyk/task-orchestrator:latest"
  ]
}'

Any MCP client — add to .mcp.json in your project root:

{
  "mcpServers": {
    "mcp-task-orchestrator": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-v", "mcp-task-data:/app/data",
        "ghcr.io/jpicklyk/task-orchestrator:latest"
      ]
    }
  }
}

Restart your client. The server auto-initializes on first run — no setup required.

3. Enable workflow schemas (optional)

Mount your project's config to activate gate enforcement:

{
  "mcpServers": {
    "mcp-task-orchestrator": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "-v", "mcp-task-data:/app/data",
        "-v", "${workspaceFolder}/.taskorchestrator:/project/.taskorchestrator:ro",
        "-e", "AGENT_CONFIG_DIR=/project",
        "ghcr.io/jpicklyk/task-orchestrator:latest"
      ]
    }
  }
}

Without schemas, all 13 tools work in schema-free mode — no gates, no required notes. Add schemas when you want enforcement.

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Claude Code Plugin

The plugin adds workflow automation on top of the MCP server — skills, hooks, and an orchestrator output style.

Install:

/plugin marketplace add https://github.com/jpicklyk/task-orchestrator
/plugin install task-orchestrator@task-orchestrator-marketplace

What it adds:

Layer	What it does
Skills	Slash commands for common workflows — /task-orchestrator:create-item, /task-orchestrator:manage-schemas, /task-orchestrator:quick-start
Hooks	Automatic context injection at session start, plan mode integration, sub-agent context handoff, actor attribution enforcement
Output style	Workflow Orchestrator mode — Claude plans, delegates to sub-agents, and tracks progress without writing code directly

The MCP server works without the plugin. The plugin makes it seamless with Claude Code.

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13 MCP Tools

Category	Tools	Purpose
Graph	manage_items, query_items, create_work_tree, complete_tree	Build and query the work item hierarchy
Notes	manage_notes, query_notes	Persistent phase-scoped documentation
Dependencies	manage_dependencies, query_dependencies	Typed edges with pattern shortcuts (linear, fan-out, fan-in)
Workflow	advance_item, get_next_status, get_context, get_next_item, get_blocked_items	Trigger-based transitions with gate enforcement and dependency validation

Every tool supports short hex ID prefixes — advance_item(itemId="a3f2") instead of full UUIDs.

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What It Looks Like in Practice

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

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

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

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Documentation

Resource	What's there
Quick Start Guide	Full setup walkthrough with first work item
API Reference	All 13 tools — parameters, response shapes, actor attribution
Workflow Guide	Schemas, phase gates, dependencies, lifecycle modes
Fleet Deployment	Multi-agent operators: identity policy, SQLite tuning, capacity planning, claim disclosure
Wiki	Full documentation hub
Changelog	Release history
Contributing	Developer setup and contribution process

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Technical Stack

• Kotlin 2.2.0 with Coroutines
• SQLite + Exposed ORM — zero-config persistent storage
• Flyway Migrations — versioned schema management
• MCP SDK 0.9.0 — STDIO and HTTP transport
• Docker — one-command deployment

Clean Architecture (Domain > Application > Infrastructure > Interface) with comprehensive test coverage.

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License

MIT License — Free for personal and commercial use.