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Workflow Commander MCP Server

Workflow Commander MCP Server

Local
github.com/AndurilCodeAI & Agents5Updated 70w ago

About Workflow Commander MCP Server

Workflow Commander MCP Server guides coding agents through structured development workflows defined in YAML. It supports discovering existing workflows and generating task-specific ones, with session state and phase progression control.

Key features

  1. 01YAML-defined workflows
  2. 02Task-based workflow discovery
  3. 03Custom workflow generation
  4. 04Session state tracking
  5. 05Controlled phase progression

Use cases

  1. 01Guiding coding tasks through defined phases
  2. 02Selecting workflows for debugging or documentation
  3. 03Creating task-specific development workflows

MCP support

It exposes workflow discovery, guidance, and execution capabilities to MCP clients, including state tracking and progression control.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"workflow-commander": {
"command": "uvx",
"args": [
"git+https://github.com/AndurilCode/workflow-commander@main"
]
}
}
}
Run commanduvx 'git+https://github.com/AndurilCode/workflow-commander@main'

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypigit+https://github.com/AndurilCode/workflow-commander@main

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README
Accordo Logo

Accordo MCP Server

PyPI - Version GitHub Actions Workflow Status GitHub Release Date Quality Gate Status

A powerful MCP (Model Context Protocol) server that provides dynamic YAML-driven workflow guidance for AI coding agents. Features structured development workflows with progression control and decision points.

What This Does

This server guides AI agents through structured, schema-driven development workflows:

  • 📋 Dynamic YAML Workflows: Custom workflows defined in YAML with schema-driven execution
  • 🔍 Discovery-First: Automatically discovers and selects appropriate workflows based on task
  • ⚡ Real-time State: Live workflow state tracking with comprehensive session management
  • 🎯 Mandatory Guidance: Authoritative phase-by-phase instructions agents must follow
  • 🤖 Agent-Generated Workflows: Unique capability for agents to create custom YAML workflows on-the-fly
Two Workflow Approaches
Built-in Workflow Discovery Process
  1. Automatic Discovery - System scans .accordo/workflows/ for available YAML workflows
  2. Intelligent Matching - Analyzes task description against workflow capabilities and goals
  3. Workflow Selection - Agent chooses the most appropriate workflow from discovered options
  4. Immediate Execution - Selected workflow starts with full state management and progression control

This provides instant access to proven workflows for common development patterns - coding, documentation, debugging, and more!

Agent Workflow Generation Process
  1. Analyze Task Requirements - Agent understands specific needs
  2. Get Creation Guidance - Comprehensive templates and best practices provided
  3. Generate Custom YAML - Agent creates workflow tailored to exact requirements
  4. Start Execution - Custom workflow runs with full state management

This enables agents to create workflows for any domain - web development, data science, DevOps, research, documentation, testing, and more!

Quick Start

Installing accordo CLI

The accordo CLI provides easy configuration and management of MCP servers. Install it directly with:

# Download and run the installation script
curl -fsSL https://raw.githubusercontent.com/AndurilCode/accordo/refs/heads/main/install.sh | bash

The installer will:

  • ✅ Detect your environment (virtual env, available tools)
  • 🌍 Global installation (recommended): Uses pipx for system-wide access
  • 📦 Virtual environment: Uses uv or pip if you're in a venv
  • 🔧 Auto-setup: Handles dependencies and PATH configuration
CLI Usage
# Configure MCP servers interactively
accordo configure

# Quick setup for specific platforms
accordo configure -p cursor -y
accordo configure -p claude-code -y

# Deploy workflow guidelines to AI assistants
accordo bootstrap-rules              # All assistants
accordo bootstrap-rules cursor       # Cursor only
accordo bootstrap-rules --force all  # Overwrite existing

# List supported platforms and manage servers
accordo list-platforms
accordo list-servers -p cursor
accordo validate -p cursor

# Get help
accordo --help
Manual MCP Configuration

Alternatively, install uv for Python package management and configure your MCP client manually: For Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "accordo": {
      "command": "uvx",
      "args": ["accordo-workflow-mcp"]
    }
  }
}

Or use the following link to install the MCP server (Cursor Installed):

Install MCP Server

Getting Help

📚 Documentation
🎯 Examples

License

This project is licensed under the MIT License - see the LICENSE file for details.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.