aceflow-mcp-server 2.1.3

AceFlow MCP Server - AI-协作增强版，支持双向AI-MCP数据交换的智能开发工作流服务器，支持HTTP和stdio传输模式

AceFlow MCP Server

AI-driven workflow management through Model Context Protocol.

📁 Project Structure

aceflow-mcp-server/
├── aceflow_mcp_server/          # Core package directory
│   ├── core/                    # Core functionality modules
│   ├── main.py                  # Main entry point
│   ├── tools.py                 # Tool implementations
│   └── ...
├── tests/                       # Formal test suite
├── examples/                    # Examples and demo code
├── scripts/                     # Build and deployment scripts
│   ├── build/                   # Build-related scripts
│   ├── deploy/                  # Deployment scripts
│   └── dev/                     # Development tools
├── docs/                        # Documentation
│   ├── user-guide/              # User guides
│   ├── developer-guide/         # Developer guides
│   └── project/                 # Project documentation
├── dev-tests/                   # Development tests and experiments
└── pyproject.toml               # Project configuration

Overview

AceFlow MCP Server provides structured software development workflows through the Model Context Protocol (MCP), enabling AI clients like Kiro, Cursor, and Claude to manage projects with standardized processes.

Features

🛠️ MCP Tools

• aceflow_init: Initialize projects with different workflow modes
• aceflow_stage: Manage project stages and workflow progression
• aceflow_validate: Validate project compliance and quality
• aceflow_template: Manage workflow templates

📊 MCP Resources

• aceflow://project/state: Current project state and progress
• aceflow://workflow/config: Workflow configuration and settings
• aceflow://stage/guide/{stage}: Stage-specific guidance and instructions

🤖 MCP Prompts

• workflow_assistant: Context-aware workflow guidance
• stage_guide: Stage-specific assistance and best practices

Quick Start

Installation

# Method 1: Install via uvx (recommended for end users)
uvx aceflow-mcp-server

# Method 2: Install via pip (traditional method)
pip install aceflow-mcp-server

# Method 3: Install with optional features
pip install aceflow-mcp-server[performance,monitoring]

MCP Client Configuration

For uvx installation:

{
  "mcpServers": {
    "aceflow": {
      "command": "uvx",
      "args": ["aceflow-mcp-server@latest"],
      "env": {
        "ACEFLOW_LOG_LEVEL": "INFO"
      }
    }
  }
}

For pip installation:

{
  "mcpServers": {
    "aceflow": {
      "command": "aceflow-mcp-server",
      "args": [],
      "env": {
        "ACEFLOW_LOG_LEVEL": "INFO"
      }
    }
  }
}

Usage Example

User: "I want to start a new AI project with standard workflow"

AI: I'll help you initialize a new project using AceFlow.

[Uses aceflow_init tool]
✅ Project initialized successfully in standard mode!

Current status:
- Project: ai-project
- Mode: STANDARD
- Stage: user_stories (0% complete)

Ready to begin with user story analysis. Would you like guidance for this stage?

Workflow Modes

Minimal Mode

Fast prototyping and concept validation

• 3 stages: Implementation → Test → Demo
• Ideal for MVPs and quick experiments

Standard Mode

Traditional software development workflow

• 8 stages: User Stories → Task Breakdown → Test Design → Implementation → Unit Test → Integration Test → Code Review → Demo
• Balanced approach for most projects

Complete Mode

Enterprise-grade development process

• 12 stages: Full requirements analysis through security review
• Comprehensive quality gates and documentation

Smart Mode

AI-enhanced adaptive workflow

• 10 stages with intelligent adaptation
• Dynamic complexity assessment and optimization

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   AI Client     │    │  MCP Server     │    │  AceFlow Core   │
│  (Kiro/Cursor)  │◄──►│   (FastMCP)     │◄──►│    Engine       │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                              │
                              ▼
                       ┌─────────────────┐
                       │  File System    │
                       │ (.aceflow/...)  │
                       └─────────────────┘

Development

Setup

# Clone repository
git clone https://github.com/aceflow/aceflow-mcp-server
cd aceflow-mcp-server

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=aceflow_mcp_server

Project Structure

aceflow-mcp-server/
├── aceflow_mcp_server/
│   ├── __init__.py
│   ├── server.py          # Main MCP server
│   ├── tools.py           # MCP tools implementation
│   ├── resources.py       # MCP resources
│   ├── prompts.py         # MCP prompts
│   └── core/              # Core functionality
├── tests/                 # Test suite
├── docs/                  # Documentation
└── pyproject.toml         # Project configuration

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

• Documentation: https://docs.aceflow.dev/mcp
• Issues: https://github.com/aceflow/aceflow-mcp-server/issues
• Discussions: https://github.com/aceflow/aceflow-mcp-server/discussions

---

Generated by AceFlow v3.0 MCP Server