Workflow Discipline Engine
Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with
What is Workflow Discipline Engine?
Workflow Discipline Engine is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to server-enforced workflow discipline for ai agents. an mcp server providing persistent work items, dependency graphs, quality gates, and actor attribution. schemas define what agents must produce — the...
Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Server-enforced workflow discipline for AI agents. An MCP se
Use Cases
Maintainer
Works with
Installation
NPM
npx -y task-orchestratorManual Installation
npx -y task-orchestratorConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Workflow Discipline Engine
Task Orchestrator is an MCP server that enforces structured workflow discipline on AI agents by providing persistent work items, dependency graphs, quality gates, and actor attribution backed by a SQLite database. Unlike in-memory task lists, it blocks agent transitions if schema rules or dependency constraints aren't satisfied — ensuring agents produce what they're supposed to before moving on. It runs as a Docker container and integrates with Claude Code, Claude Desktop, and any MCP-compatible client, making it ideal for multi-session and multi-agent development projects where continuity and auditability matter.
Prerequisites
- Docker installed and running (the server ships as a Docker image, not a native binary)
- An MCP-compatible client: Claude Code, Claude Desktop, or any client supporting stdio MCP
- Git (optional, for mounting project-level schema config files)
- Basic familiarity with Docker volumes for persisting the SQLite database between sessions
Pull the Docker image
Download the latest Task Orchestrator image from the GitHub Container Registry. This image contains the Kotlin-based MCP server and its SQLite runtime.
docker pull ghcr.io/jpicklyk/task-orchestrator:latestRegister with Claude Code via the CLI
Add the Task Orchestrator to Claude Code's MCP configuration. The -v flag mounts a named Docker volume so the SQLite database persists across container restarts.
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"]
}'Or add to .mcp.json for other clients
For Claude Desktop or other MCP clients, add the server configuration to your .mcp.json or claude_desktop_config.json file.
{
"mcpServers": {
"mcp-task-orchestrator": {
"command": "docker",
"args": ["run", "--rm", "-i", "-v", "mcp-task-data:/app/data",
"ghcr.io/jpicklyk/task-orchestrator:latest"]
}
}
}(Optional) Enable schema enforcement with a project config
Mount a .taskorchestrator/ directory from your project to activate workflow gates. The AGENT_CONFIG_DIR env var tells the server where to find config.yaml with your custom schemas and gate rules.
{
"mcpServers": {
"mcp-task-orchestrator": {
"command": "docker",
"args": ["run", "--rm", "-i",
"-v", "mcp-task-data:/app/data",
"-v", "/your/project/.taskorchestrator:/project/.taskorchestrator:ro",
"-e", "AGENT_CONFIG_DIR=/project",
"ghcr.io/jpicklyk/task-orchestrator:latest"]
}
}
}Start using the 14 MCP workflow tools
Once connected, the AI agent can call manage_items, advance_item, manage_dependencies, get_context, get_next_item, claim_item, and more. The server enforces gate rules and dependency ordering automatically.
Workflow Discipline Engine Examples
Client configuration (Claude Desktop / .mcp.json)
Minimal working config using a named Docker volume for persistence. No additional environment variables are required for basic operation.
{
"mcpServers": {
"mcp-task-orchestrator": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"-v", "mcp-task-data:/app/data",
"ghcr.io/jpicklyk/task-orchestrator:latest"
]
}
}
}Prompts to try
Example prompts that exercise Task Orchestrator's workflow and dependency management tools.
- "Get my current work context — what items are active, blocked, or pending?"
- "Create a new work item for implementing user authentication and add it to the backlog"
- "Mark task a3f2 as started and assign it to me"
- "Show all blocked items and explain what they're waiting on"
- "Add a note to item b7c1 documenting the design decision we just made"
- "What's the next available task I should work on?"Troubleshooting Workflow Discipline Engine
Docker container exits immediately when launched by the MCP client
Ensure the -i flag is present in the docker run args — it keeps stdin open for stdio MCP communication. Without it the container exits right away. Also verify Docker is running before starting your MCP client.
Work items are lost between sessions
Confirm the named volume mcp-task-data is being mounted with -v mcp-task-data:/app/data. Without this volume mount, the container's SQLite database is discarded when --rm removes the container. Run docker volume ls to verify the volume exists.
Schema validation errors when agents try to advance work items
Schema enforcement is active when AGENT_CONFIG_DIR is set and a config.yaml exists in .taskorchestrator/. Review the gate rules in your config.yaml and ensure the agent has populated all required fields before calling advance_item.
Frequently Asked Questions about Workflow Discipline Engine
What is Workflow Discipline Engine?
Workflow Discipline Engine is a Model Context Protocol (MCP) server that server-enforced workflow discipline for ai agents. an mcp server providing persistent work items, dependency graphs, quality gates, and actor attribution. schemas define what agents must produce — the server blocks the call if they don't. works with It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Workflow Discipline Engine?
Install via npm with the command: npx -y task-orchestrator. Then add the server configuration to your AI client's JSON config file (e.g., claude_desktop_config.json or .cursor/mcp.json).
Which AI clients work with Workflow Discipline Engine?
Workflow Discipline Engine works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Workflow Discipline Engine free to use?
Yes, Workflow Discipline Engine is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
Workflow Discipline Engine Alternatives — Similar Coding Agents Servers
Looking for alternatives to Workflow Discipline Engine? Here are other popular coding agents servers you can use with Claude, Cursor, and VS Code.
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