AI Software Engineering Team - MCP Multi-Agent System

Advanced AI-powered software development automation system built on the Model Context Protocol (MCP)

A sophisticated multi-agent AI system that simulates an entire software engineering team, capable of taking a simple project idea and transforming it into a complete, production-ready software project with full documentation, testing, and deployment configuration.

Architecture Overview

This system consists of 8 specialized AI agents working together through an intelligent orchestrator:

• Product Analyst - Requirements analysis & user stories
• Research Engineer - Web research & best practices
• Software Architect - System design & technology stack
• Technical Lead - Implementation planning & task breakdown
• Senior Developer - Production code implementation
• QA Engineer - Testing & quality assurance
• DevOps Engineer - CI/CD & deployment infrastructure
• Documentation Specialist - Documentation & guides

Quick Start

Prerequisites

• Python 3.11+
• Node.js (for MCP Inspector)
• API Keys: Tavily Search, Google Gemini

Installation

1. Clone the repository

   git clone https://github.com/yourusername/ai-software-engineering-team-mcp.git
   cd ai-software-engineering-team-mcp
   

2. Install dependencies

   pip install -r requirements.txt
   # or using uv
   uv sync
   

3. Set up environment variables

   cp .env.example .env
   # Edit .env with your API keys
   

4. Start the servers

   # Terminal 1: Start MCP Server
   python server.py
   
   # Terminal 2: Start FastAPI Server
   python fastapi_server.py
   

API Endpoints

FastAPI Server (Port 8002)

• GET / - Service status and team information
• GET /health - Health check with service status
• GET /tools - List all available MCP tools
• GET /project - Current project status
• GET /docs - Interactive API documentation

MCP Server (Port 8000)

• Direct MCP protocol access for AI tools and clients

Usage Examples

Simple Project Request

curl -X POST http://localhost:8002/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "method": "tools/call",
    "params": {
      "name": "orchestrator",
      "arguments": {
        "user_request": "Build a todo list app with React and Node.js"
      }
    }
  }'

Complex Project Request

curl -X POST http://localhost:8002/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "method": "tools/call",
    "params": {
      "name": "orchestrator",
      "arguments": {
        "user_request": "Build an e-commerce platform with user authentication, product catalog, shopping cart, and payment integration using React, Node.js, and PostgreSQL",
        "execution_mode": "full"
      }
    }
  }'

Available Tools

Tool	Description
orchestrator	Main coordinator that manages the entire team workflow
product_analyst	Analyzes requirements and creates user stories
research_engineer	Performs web research and finds best practices
software_architect	Designs system architecture and tech stack
technical_lead	Creates implementation plans and task breakdown
senior_developer	Writes production-ready code
qa_engineer	Creates comprehensive test suites
devops_engineer	Sets up CI/CD and deployment configuration
documentation_specialist	Creates documentation and guides
export_project_files	Exports complete project to file system
team_status	Shows current team and project status
reset_project	Resets project state for new project

Project Structure

Configuration

Environment Variables

# Required API Keys
TAVILY_API_KEY=your_tavily_api_key_here
GEMINI_API_KEY=your_gemini_api_key_here

# Server Configuration
PORT=8000  # MCP Server port

Execution Modes

• "full" - All 8 team members (complete project)
• "planning" - Analysis, research, architecture only
• "implementation" - Adds code implementation
• "deployment" - Adds DevOps configuration
• "custom" - AI decides based on complexity

Testing

Test the MCP Server

# Check server status
curl http://localhost:8000/health

# List available tools
curl http://localhost:8002/tools

Test with MCP Inspector

npx @modelcontextprotocol/inspector

Features

• End-to-End Automation - From idea to deployable code
• Multi-Agent Coordination - 8 specialized AI agents
• Intelligent Decision Making - Adapts workflow based on complexity
• Production-Ready Output - Generates actual, usable code
• Dual Protocol Support - Both MCP and REST API access
• Live Research Integration - Real-time web search capabilities
• Complete Project Export - Full file system generation
• Interactive Documentation - Built-in API docs

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Acknowledgments

• Built on the Model Context Protocol (MCP)
• Powered by Google Gemini and Tavily Search
• FastAPI integration for REST API access

Support

• Email: ellhaweet@gmail.com

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Made with care by the AI Software Engineering Team