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mcp-ecosystem-platform

πŸš€ Ultimate Developer Productivity Suite - 11 specialized MCP servers for AI-powered code analysis, security scanning, browser automation, and workflow orchestration. FastAPI + React + TypeScript + Docker ready.

Install / Use

claude mcp add turtir-ai -- npx -y github:turtir-ai/mcp-ecosystem-platform

If the server publishes to npm under a different name, use that package instead β€” check the repo README.

About this skill
πŸ”Œ

MCP Server

Model Context Protocol server

Quality Score

75/100

Category

Security

Supported Platforms

Claude Code
Claude Desktop
Zed

πŸš€ MCP Ecosystem Platform

<div align="center">

MCP Ecosystem Platform Demo

Ultimate Developer Productivity Suite - A comprehensive platform built around 11 specialized MCP (Model Context Protocol) servers, providing AI-powered code analysis, security scanning, browser automation, and intelligent workflow orchestration.

GitHub Stars GitHub Forks GitHub Issues GitHub PRs

CI/CD Pipeline License: MIT Python Node.js

FastAPI React TypeScript Docker Kubernetes

MCP Protocol AI Powered Security First

</div>

🌟 Key Features

πŸ€– AI-Powered Development

  • Smart Git Review: AI-driven code analysis and review automation
  • Intelligent Workflow Orchestration: Chain MCP servers for complex tasks
  • Multi-Model AI Access: Groq Llama 3.1, OpenRouter, and more
  • Real-time Code Intelligence: Context-aware suggestions and analysis

πŸ”’ Security & Monitoring

  • API Key Protection: Advanced sniffer and protection mechanisms
  • Network Analysis: Real-time traffic monitoring and optimization
  • Security Scanning: Continuous threat detection and vulnerability assessment
  • Automated Compliance: Security best practices enforcement

🌐 Web & Browser Automation

  • Real Browser Control: No-simulation browser automation
  • Deep Web Research: Comprehensive competitive intelligence
  • Data Extraction: Automated web scraping and analysis
  • Cross-platform Testing: Multi-browser compatibility testing

πŸ“Š Developer Dashboard

  • Unified Control Interface: Single pane of glass for all operations
  • Real-time Metrics: Performance monitoring and analytics
  • Workflow Visualization: Interactive workflow designer
  • Team Collaboration: Shared workspaces and project management

πŸ—οΈ Architecture

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚                    Frontend (React + TS)                    β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚                   FastAPI Backend                          β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚                  MCP Server Layer                          β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚  PostgreSQL  β”‚    Redis     β”‚   Docker    β”‚  Kubernetes   β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

πŸ”§ MCP Servers (11 Specialized Servers)

| Server | Purpose | Key Features | | ----------------------- | --------------- | ------------------------------------ | | kiro-tools | Core Operations | Filesystem, Git, Database operations | | groq-llm | AI Processing | Ultra-fast Llama 3.1 AI processing | | openrouter-llm | Multi-Model AI | Access to multiple AI models | | browser-automation | Web Control | Real browser automation | | deep-research | Intelligence | Comprehensive web research | | api-key-sniffer | Security | API key protection and monitoring | | network-analysis | Monitoring | Network performance analysis | | enhanced-filesystem | File Ops | Advanced file operations | | enhanced-git | Version Control | Git analysis and automation | | real-browser | Web Testing | No-simulation browser control | | simple-warp | Terminal | Terminal integration and automation |

πŸš€ Quick Start

🎯 Faz 0: Stabilizasyon Tamamlandı! Tek komutla tüm sistemi başlatabilirsiniz.

Prerequisites

  • Python 3.11+
  • Node.js 18+ (Frontend iΓ§in)
  • Git

⚑ One-Command Startup (Recommended)

git clone https://github.com/turtir-ai/mcp-ecosystem-platform.git
cd mcp-ecosystem-platform

# Edit .env with your API keys (optional for basic testing)
# Then start everything with one command:
python start-dev.py

That's it! πŸŽ‰ The script will:

  • βœ… Check prerequisites
  • πŸ“¦ Install dependencies automatically
  • πŸš€ Start all services in the correct order
  • πŸ”§ Fix VS Code extension connection issues

🌐 Access the Platform

  • Frontend: http://localhost:3000
  • Backend API: http://localhost:8001
  • API Documentation: http://localhost:8001/docs
  • Health Check: http://localhost:8001/health
  • MCP Status: http://localhost:8001/api/v1/mcp/status
  • MCP Manager: http://localhost:8009

πŸ”§ Port Standardization

| Service | Port | URL | |---------|------|-----| | Frontend | 3000 | http://localhost:3000 | | Backend API | 8001 | http://localhost:8001 | | MCP Manager | 8009 | http://localhost:8009 |

πŸ› οΈ Manual Setup (Advanced)

<details> <summary>Click to expand manual setup instructions</summary>

1. Clone & Setup

git clone https://github.com/turtir-ai/mcp-ecosystem-platform.git
cd mcp-ecosystem-platform

# Copy environment template
cp .env.example .env
# Edit .env with your API keys

2. Backend Setup

cd backend
python -m venv venv

# Windows
venv\Scripts\activate
# Linux/Mac
source venv/bin/activate

pip install -r requirements.txt

3. Frontend Setup

cd frontend
npm install

4. Start Services Manually

# Terminal 1: Backend
cd backend
uvicorn app.main:app --reload --port 8001

# Terminal 2: Frontend
cd frontend
npm start

# Terminal 3: MCP Manager
python mock-api-server.py
</details>

πŸ“ Project Structure

mcp-ecosystem-platform/
β”œβ”€β”€ πŸ“ backend/                 # FastAPI backend
β”‚   β”œβ”€β”€ πŸ“ app/
β”‚   β”‚   β”œβ”€β”€ πŸ“ core/           # Core interfaces and config
β”‚   β”‚   β”œβ”€β”€ πŸ“ services/       # Business logic services
β”‚   β”‚   β”œβ”€β”€ πŸ“ api/            # API routes
β”‚   β”‚   β”œβ”€β”€ πŸ“ models/         # Database models
β”‚   β”‚   └── πŸ“„ main.py         # FastAPI application
β”‚   β”œβ”€β”€ πŸ“ tests/              # Backend tests
β”‚   β”œβ”€β”€ πŸ“„ requirements.txt    # Python dependencies
β”‚   └── πŸ“„ Dockerfile          # Backend container
β”œβ”€β”€ πŸ“ frontend/               # React frontend
β”‚   β”œβ”€β”€ πŸ“ src/
β”‚   β”‚   β”œβ”€β”€ πŸ“ components/     # React components
β”‚   β”‚   β”œβ”€β”€ πŸ“ pages/          # Page components
β”‚   β”‚   β”œβ”€β”€ πŸ“ services/       # API clients
β”‚   β”‚   └── πŸ“ types/          # TypeScript types
β”‚   β”œβ”€β”€ πŸ“„ package.json        # Node dependencies
β”‚   └── πŸ“„ Dockerfile.dev      # Frontend container
β”œβ”€β”€ πŸ“ mcp-servers/            # MCP server configurations
β”œβ”€β”€ πŸ“ vscode-extension/       # VS Code extension
β”œβ”€β”€ πŸ“„ docker-compose.yml      # Development environment
β”œβ”€β”€ πŸ“„ .env.example           # Environment template
└── πŸ“„ README.md              # This file

πŸ”§ Configuration

Environment Variables

# API Keys
GROQ_API_KEY=your_groq_api_key
OPENROUTER_API_KEY=your_openrouter_key
GOOGLE_API_KEY=your_google_key
BRAVE_SEARCH_API_KEY=your_brave_key

# Database
DATABASE_URL=postgresql://postgres:password@localhost:5432/mcp_platform
REDIS_URL=redis://localhost:6379/0

# Security
SECRET_KEY=your_secret_key
JWT_SECRET=your_jwt_secret

MCP Server Configuration

The platform automatically discovers and configures MCP servers from your .kiro/settings/mcp.json file.

πŸ§ͺ Testing

Backend Tests

cd backend
pytest tests/ -v --cov=app

Frontend Tests

cd frontend
npm test

Integration Tests

# Run full test suite
docker-compose -f docker-compose.test.yml up --build

πŸš€ Deployment

Docker Production

# Build and start production containers
docker-compose -f docker-compose.prod.yml up -d

Kubernetes

# Deploy to Kubernetes
kubectl apply -f k8s/

πŸ“Š Monitoring & Analytics

  • Health Checks: /health endpoint for all services
  • Metrics: Prometheus metrics at /metrics
  • Logs: Structured logging with correlation IDs
  • Tracing: Distributed tracing support

🀝 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

Development Guidelines

  • Follow PEP 8 for Python code
  • Use TypeScript for all frontend code
  • Write comprehensive tests
  • Update documentation
  • Follow conventional commits

πŸ€– AI ile Sistem YΓΆnetimi

MCP Ecosystem Platform, AI destekli proaktif sistem yânetimi sunar. AI, sistem sağlığını sürekli izler ve sorunları otomatik olarak tespit ederek çâzüm ânerileri sunar.

πŸ” AkΔ±llΔ± Sistem Δ°zleme

AI sistemi şu bileşenleri sürekli izler:

  • MCP SunucularΔ±: TΓΌm 11 MCP sunucusunun durumu ve performansΔ±
  • Sistem KaynaklarΔ±: CPU, bellek, disk kullanΔ±mΔ±
  • VeritabanΔ± PerformansΔ±: Sorgu sΓΌreleri ve bağlantΔ± havuzu durumu
  • Ağ BağlantΔ±larΔ±: API yanΔ±t sΓΌreleri ve bağlantΔ± durumu

🚨 Proaktif Hata Tespiti

AI, şu pattern'leri otomatik olarak tespit eder:

  • Tekrarlayan Hatalar: Belirli aralΔ±klarla tekrarlanan sistem hatalarΔ±
  • Performans Düşüşü: Zaman iΓ§inde artan yanΔ±t sΓΌreleri
  • Kaynak TΓΌkenmesi: Kritik eşiklere yaklaşan sistem kaynaklarΔ±
  • Cascade Hatalar: Birden fazla bileşeni etkileyen zincirleme hatalar

πŸ› οΈ AI Eylem Γ–nerileri

Tespit edilen sorunlar için AI şu eylemleri ânerebilir:

🟒 Otomatik Onaylı (Düşük Risk)

  • Sistem durumu sorgulama
  • Log dosyalarΔ±nΔ± okuma
  • Performans metriklerini toplama
  • SΓΌreΓ§ analizi yapma

🟑 Kullanıcı Onayı Gerekli (Orta Risk)

  • Dosya dΓΌzenleme işlemleri
  • KonfigΓΌrasyon değişiklikleri
  • Otomatik dΓΌzeltme uygulama
  • Git işlemleri

πŸ”΄ YΓΌksek Riskli (AΓ§Δ±k Onay Gerekli)

  • MCP sunucu yeniden

Truncated for display β€” read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars10
CategorySecurity
Updated1y ago
Forks3

Languages

Python

Security Score

85/100

Audited on Jul 25, 2025

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