Structured Workflow Server enforces disciplined, multi-phase programming workflows for AI-assisted development. It guides AI assistants through systematic audit, analysis, and verification stages before allowing code changes, preventing common pitfalls like duplicate implementations, orphaned code, or broken tests. Key features: - Mandatory audit and inventory phases that require AI to understand existing code structure before making changes - Structured workflow phases for refactoring, feature development, and testing with defined entry and exit criteria - Lint-iterate-lint cycles that require iterative refinement and verification before proceeding - Session state management to prevent skipping phases or jumping directly to implementation - Verified output requirements at each phase to ensure thorough understanding and quality results - Reduces need for repetitive prompting by automatically enforcing systematic development practices

Structured Workflow MCP Server

[](https://coff.ee/jasonholtdigital) [](https://smithery.ai/server/@kingdomseed/structured-workflow-mcp)

NOTE: I am not currently working on this or actively maintaining it. I learned a few things about prompting and agents while making this MCP server. It has a lot of very valuable ideas that could be used or improved upon as an MCP server but I'm also looking at ways to incorporate the core ideas into Agents, for example, in Claude. The core idea here is that AI should follow specific, pre-determined steps to solving a problem, just like we as humans might do and there may be other ways to achieve this apart from this MCP server.

An MCP server that enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development.

Why I Built This

TLDR: I got tired of repeating "inventory and audit first" across every AI platform and prompt, so I built an MCP server that automatically enforces this disciplined approach. It forces AI to think systematically and follow structured phases instead of jumping straight into code changes.

So I've built an MCP server that fits into my workflow and thinking process while I'm programming. I made it available via npx and you can download it yourself if you want something local.

In essence I was doing some repeated tasks with AI where I wanted it to complete refactoring work for part of a larger project. I was struggling because it was often missing or glossing over key things: classes or systems that already exist (a preferences service for example), creating duplicates of things, or when correcting mistakes, leaving orphaned unused methods/code around places, and when writing tests it would often pull in the wrong imports or put these together in the wrong way resulting in syntax errors but would jump straight into writing the next test without fixing the first one that was broken.

I sort of stumbled on this idea of the model needing to perform an audit and inventory of the current project (or not even the whole project--just one layer or feature in a project) before moving to any kind of implementation phase and it needed a lint iterate lint phase. I tried this with rules with limited success and then prompting with much better success but I was constantly repeating myself.

So I started noodling on this idea of an MCP server that forced the AI to work through a problem in phases or lanes. So that's what this does. There's a number of different workflow styles and I'm open to any other ideas or improvements.

Feel free to check it out if it helps your use case. It's a work in progress but it has been doing a pretty great job for what I'm using it for now. Happy to share more if you are interested.

Features

Enforced Workflow Phases - AI must complete specific phases in order (setup, audit, analysis, planning, implementation, testing, etc.)

Mandatory Output Artifacts - Each phase requires structured documentation or verified outputs before proceeding

Multiple Workflow Types:

Output Verification - The server validates that outputs contain meaningful content and proper structure

Session State Management - Tracks progress and prevents skipping phases

How It Works

Here's how the AI moves through a structured workflow:

graph TD
    A[🚀 Start Workflow] --> B[AI Gets Phase Guidance]
    B --> C{Create Phase Output}
    C --> D[Auto-Save with Numbered Naming00-setup-confirmation-2025-01-07.md]
    D --> E[Phase Validation]
    E --> F{All Phases Done?}
    F -->|No| G[Move to Next Phase]
    G --> B
    F -->|Yes| H[Workflow Complete!]
    
    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#fff3e0
    style D fill:#e8f5e8
    style E fill:#fff9c4
    style H fill:#e8f5e8

What happens at each step: 1. Start Workflow - AI calls a workflow tool (refactor_workflow, create_feature_workflow, etc.) 2. AI Gets Phase Guidance - Server provides specific instructions for current phase (audit, analyze, implement, etc.) 3. Create Phase Output - AI works through the phase and creates documentation/artifacts 4. Auto-Save - Files are automatically saved with numbered naming in task directories 5. Phase Validation - Server validates outputs meet requirements before proceeding 6. Next Phase - Process repeats until workflow is complete

One benefit of this breakdown is that the AI agent receive instruction sets that are relevant to the current phase and not the