Guía de Características Avanzadas¶

Esta guía detalla las funciones avanzadas de seguridad, redes aisladas, despliegue, depuración, escalado automático y la integración de EasyPanel MCP con IDEs modernos.

Características de Seguridad¶

Herramientas de Seguridad Disponibles¶

EasyPanel MCP ofrece las siguientes capacidades relacionadas con la seguridad:

Característica	Estado	Descripción
Autenticación por API Key	Disponible	Autenticación segura mediante Bearer token
Autenticación por Email/Password	Disponible	Método de autenticación alternativo
Gestión de Sesiones	Disponible	Manejo automático del token de sesión
Verificación SSL/TLS	Disponible	Verificación SSL configurable
Redes Internas	Vía Config de Servicio	Redes aisladas a través de la configuración del servicio
Autenticación Básica (Basic Auth)	Disponible	Autenticación básica HTTP para servicios
Gestión de Dominios	Disponible	Configuración de dominios personalizados
Control de Exposición de Puertos	Disponible	Gestión de puertos expuestos

Security Best Practices¶

1. Use API Keys (Recommended)¶

# Generate API key in EasyPanel Dashboard
# Settings → API Keys → Create New Key

# Use in .env
EASYPANEL_API_KEY=ep_live_xxxxxxxxxxxxx

Advantages: - More secure than password - Can be rotated easily - Can be revoked without changing password - No plaintext password storage

2. Isolated Networks (Internal Services)¶

EasyPanel manages networks automatically. To create isolated services:

// Ask your AI agent:
"Create a PostgreSQL service in an isolated network"

// The AI will configure:
{
  "name": "postgres-db",
  "projectId": "your-project",
  "sourceImage": "postgres:15",
  "internal": true  // ← Isolated from internet
}

What happens: - EasyPanel creates an internal Docker network - Service is only accessible by other services in the same network - No public exposure

3. Environment Variables Security¶

# Ask AI to set sensitive env vars:
"Set DATABASE_URL environment variable for my-api service"

// The AI will use:
services.app.updateEnv with encrypted values

4. Port Management¶

Control which ports are exposed:

// Ask your AI:
"Expose port 8080 for my-api service but keep database internal"

// Configuration:
{
  "ports": [
    {"public": 80, "private": 8080, "protocol": "http"}
  ]
}

Redes Aisladas¶

How EasyPanel Handles Networks¶

EasyPanel automatically manages Docker networks. You don't need to create networks manually.

Network Types:

Type	Description	Use Case
Public Network	Services accessible from internet	Web apps, APIs
Internal Network	Services only accessible internally	Databases, caches
Project Network	Services within same project can communicate	Microservices

Creating Isolated Services¶

Method 1: Ask Your AI Agent¶

User: "Create a Redis database that's only accessible internally"

AI: 📦 Creating Redis service...
    🔗 Configuring internal network...
    ✅ Service 'redis-cache' created (internal only)
    🔒 Not exposed to internet

Method 2: Manual Configuration¶

{
  "name": "redis-internal",
  "projectId": "proj_xxx",
  "sourceImage": "redis:7",
  "env": {
    "REDIS_PASSWORD": "secure_password"
  },
  "ports": [],  // ← No public ports = internal only
  "internal": true
}

Service-to-Service Communication¶

Services in the same project can communicate automatically:

┌─────────────┐         ┌──────────────┐
│  API App    │ ──────► │  PostgreSQL  │
│  (Public)   │  Internal│  (Private)   │
└─────────────┘  Network└──────────────┘

Example:

# API can connect to database via:
DATABASE_URL=postgres://user:pass@postgres-db:5432/mydb

# But database is NOT accessible from internet

Características de Despliegue¶

Available Deployment Tools¶

Tool	Description	Example
`create_service`	Create and deploy a service	Deploy Docker image
`deploy_service`	Redeploy existing service	Apply new configuration
`update_source_image`	Update Docker image	Deploy new version
`update_source_github`	Connect GitHub repo	Auto-deploy from Git
`update_source_git`	Connect Git repository	Deploy from Git URL
`update_env`	Update environment variables	Change config
`update_resources`	Update CPU/Memory	Scale resources
`restart_service`	Restart service	Apply changes
`start_service` / `stop_service`	Control service state	Maintenance mode

Deployment Methods¶

1. Docker Image Deployment¶

User: "Deploy nginx latest version in my project"

AI: 🚀 Deploying nginx:latest...
    ✅ Service created
    🌐 Available at: https://nginx.your-domain.com

2. GitHub Deployment¶

User: "Deploy my GitHub repository myuser/myapp"

AI: 🔗 Connecting to GitHub...
    📦 Cloning repository...
    🏗️ Building from Dockerfile...
    ✅ Deployed successfully!

3. Git URL Deployment¶

User: "Deploy from https://gitlab.com/user/repo.git"

AI: 📥 Fetching from GitLab...
    🔨 Building...
    ✅ Deployment complete!

Deployment Workflow¶

1. Create Service
   ↓
2. Configure Source (Image/Git/GitHub)
   ↓
3. Set Environment Variables
   ↓
4. Configure Ports
   ↓
5. Deploy
   ↓
6. Monitor Logs

Características de Depuración¶

Available Debugging Tools¶

Tool	Description	Usage
`get_service_logs`	Get service logs	Debug issues
`inspect_service`	Get service details	View configuration
`get_system_stats`	System monitoring	Check resources
`get_service_stats`	Service metrics	Performance data

Debugging with AI¶

Example 1: Service Not Starting¶

User: "My API service won't start, what's wrong?"

AI: 🔍 Inspecting service...
    📊 Checking logs...

    ❌ Error found:
    - Port 3000 already in use
    - Another service is using this port

    💡 Solution:
    Would you like me to:
    1. Change the port to 3001?
    2. Stop the conflicting service?

Example 2: Database Connection Failed¶

User: "My app can't connect to the database"

AI: 🔍 Checking configuration...
    📊 Testing connection...

    ❌ Error: Connection refused
    🔍 Root cause: DATABASE_URL is incorrect

    Current: postgres://localhost:5432/db
    Should be: postgres://postgres-db:5432/db

    💡 Fix: Update DATABASE_URL environment variable?

Example 3: High Memory Usage¶

User: "Is my service running okay?"

AI: 📊 Checking system stats...

    ⚠️ Warning detected:
    - Memory usage: 95% (1.9GB / 2GB)
    - CPU usage: 45%

    💡 Recommendations:
    1. Increase memory limit to 4GB
    2. Check for memory leaks
    3. Enable horizontal scaling

    Would you like me to increase resources?

Log Analysis¶

User: "Show me the last 100 lines of logs for my-api"

AI: 📜 Retrieving logs...

    2026-03-14 10:30:15 INFO  Starting API server...
    2026-03-14 10:30:16 INFO  Connected to database
    2026-03-14 10:30:17 INFO  Server listening on port 8080
    2026-03-14 10:31:00 ERROR Request timeout for /api/users
    2026-03-14 10:31:01 WARN  High latency detected

    🔍 Analysis:
    - 2 errors in last hour
    - Timeout issues on /api/users endpoint
    - Possible database performance issue

Características de Escalado¶

Scaling Options in EasyPanel¶

Type	Description	Status
Vertical Scaling	Increase CPU/Memory	✅ Available
Horizontal Scaling	Multiple replicas	⚠️ Via Service Config
Auto-Scaling	Scale based on metrics	⚠️ Manual trigger

Vertical Scaling (Resources)¶

User: "My service needs more resources, scale it up"

AI: 📊 Current resources:
    - CPU: 1 core
    - Memory: 2GB

    📈 Scaling up to:
    - CPU: 2 cores
    - Memory: 4GB

    ✅ Resources updated!
    🔄 Service restarting with new limits...

Horizontal Scaling (Replicas)¶

User: "I need 3 replicas of my-api service"

AI: 📊 Current replicas: 1
    📈 Scaling to: 3 replicas

    ✅ Replica 1: Running
    ✅ Replica 2: Running
    ✅ Replica 3: Running

    🔄 Load balancer configured

Monitoring-Based Scaling¶

User: "Check if my service needs scaling"

AI: 📊 Analyzing metrics...

    Current usage (last 24h):
    - CPU: 85% average, 95% peak
    - Memory: 78% average, 92% peak
    - Requests: 1000/min average

    ⚠️ Recommendation: Scale up

    Proposed configuration:
    - CPU: 2 → 4 cores
    - Memory: 4GB → 8GB

    Proceed with scaling?

Manual Auto-Scaling Script¶

You can create automation scripts:

# Example: Check and scale based on CPU usage
async def auto_scale():
    stats = await client.get_system_stats()
    cpu_usage = stats['cpuInfo']['usedPercentage']

    if cpu_usage > 80:
        print("⚠️ High CPU usage, scaling up...")
        await client.update_service(service_id, {
            "cpu": current_cpu * 2
        })

Integración con IDEs (Antigravity/Cursor)¶

What is Antigravity IDE?¶

Antigravity (also known as Cursor or similar AI-powered IDEs) is an AI-first code editor that can integrate with MCP servers to provide context-aware assistance.

Can You Use EasyPanel MCP in Antigravity?¶

Yes! If Antigravity supports MCP (like Cursor, Cline, and other AI IDEs), you can use EasyPanel MCP.

Configuration for Antigravity/Cursor¶

Step 1: Find Config File¶

# macOS
~/Library/Application Support/Cursor/User/settings.json

# Windows
%APPDATA%\Cursor\User\settings.json

# Linux
~/.config/Cursor/User/settings.json

Step 2: Add MCP Configuration¶

{
  "mcpServers": {
    "easypanel": {
      "command": "python",
      "args": ["/absolute/path/to/easypanel-mcp/src/server.py"],
      "cwd": "/absolute/path/to/easypanel-mcp",
      "env": {
        "EASYPANEL_URL": "https://your-easypanel.com",
        "EASYPANEL_API_KEY": "ep_live_xxxxxxxxxxxxx"
      }
    }
  }
}

Step 3: Reload IDE¶

Press Ctrl+Shift+P (or Cmd+Shift+P on macOS)
Type "Reload Window"
Press Enter

Using EasyPanel MCP in Antigravity¶

Example Workflow: Fix and Deploy¶

User: "My production service is failing, check the logs and fix it"

AI (in Antigravity):
🔍 Connecting to EasyPanel...
📜 Retrieving logs for 'production-api'...

Analysis:
- Error: OutOfMemoryException
- Memory limit: 2GB
- Current usage: 1.98GB

💡 Solution found:
1. Increase memory to 4GB
2. Deploy updated version

Proceeding with fix...
✅ Memory updated: 2GB → 4GB
🚀 Service redeployed
✅ Service is now healthy!

Example: GitHub Fix and Deploy¶

User: "Fix the bug in my GitHub repo and deploy"

AI:
📥 Cloning repository...
🔍 Analyzing code...
🐛 Bug found: Missing error handling in api/users.js

Fixing...
✅ Applied fix: Added try-catch block
📝 Committing changes...
🚀 Deploying to production...
✅ Deployment successful!

Resolución de Problemas y Despliegues de Git¶

Workflow: Fix Problem → Commit → Deploy¶

Scenario: Production Bug¶

1. User reports issue
   ↓
2. AI checks logs via MCP
   ↓
3. AI identifies root cause
   ↓
4. AI fixes code (in IDE)
   ↓
5. AI commits to GitHub
   ↓
6. AI deploys via MCP
   ↓
7. Issue resolved!

Example: Complete Fix Flow¶

User: "Users can't login, fix it and deploy"

AI (using MCP + Git):
🔍 Checking service logs...
❌ Error found: Authentication service returning 500

📥 Cloning repository...
🔍 Analyzing auth.js...
🐛 Bug found: Missing environment variable

💻 Applying fix...
✅ Added missing env check
✅ Committed: "fix: Add missing env validation in auth"

🚀 Deploying to production...
✅ Build successful
✅ Deployment complete

🧪 Testing login...
✅ Login working!

Summary:
- Root cause: Missing env validation
- Fix: Added validation + error handling
- Deployed: v1.2.3
- Status: ✅ Resolved

GitHub Integration Setup¶

To enable GitHub deployments:

User: "Connect my GitHub repository"

AI:
🔗 Connecting to GitHub...
📁 Repository: youruser/yourapp
🔑 Generating deploy key...
✅ GitHub connected!

Configuration:
- Branch: main
- Auto-deploy: Enabled
- Build command: docker build

Resumen de Disponibilidad de Características¶

Feature	Available	Notes
API Key Auth	✅ Yes	Recommended method
Email/Password Auth	✅ Yes	Alternative
Isolated Networks	✅ Yes	Via service config
Service Deployment	✅ Yes	Multiple methods
GitHub Deploy	✅ Yes	Via services.app.updateSourceGithub
Git Deploy	✅ Yes	Via services.app.updateSourceGit
Docker Image Deploy	✅ Yes	Via services.app.updateSourceImage
Log Retrieval	⚠️ Limited	Via service inspection
System Monitoring	✅ Yes	Full stats available
Resource Scaling	✅ Yes	CPU/Memory
Horizontal Scaling	⚠️ Via Config	Service configuration
Auto-Scaling	⚠️ Manual	Script-based
Domain Management	✅ Yes	Custom domains
Port Management	✅ Yes	Expose/hide ports
Environment Variables	✅ Yes	Secure management
Basic Auth	✅ Yes	HTTP auth
Service Restart	✅ Yes	Instant restart
Service Start/Stop	✅ Yes	Control state

Resolución de Problemas (Troubleshooting)¶

Feature Not Available¶

Problem: AI says a feature is not available

Solution: 1. Check if EasyPanel supports it natively 2. Some features require specific EasyPanel plans 3. Verify API permissions

Network Isolation Not Working¶

Problem: Services are still accessible from internet

Solution: 1. Ensure no public ports are configured 2. Check service configuration 3. Verify project network settings

Deployment Fails¶

Problem: Service won't deploy

Common causes: - Invalid Docker image - Missing environment variables - Resource limits exceeded - Network configuration error

Solution:

User: "Deployment failed, check why"

AI: 🔍 Analyzing deployment...
    ❌ Error: Image not found

    💡 Fix: Update image name to 'nginx:latest'

Documentación Relacionada¶

AI Agents Integration - Connect with Claude, Cursor, etc.
Configuration Guide - Setup and configuration
Tools Reference - Available MCP tools
Security Guide - Security best practices

🚀 Master advanced EasyPanel MCP features! Deploy, scale, and debug with AI assistance.