MCP Servers Overview

This document provides a comprehensive overview of all Model Context Protocol (MCP) servers integrated into the Inttrest platform for AI-powered event discovery.

🎯 Platform Integration

The Inttrest platform leverages four specialized MCP servers to provide comprehensive event discovery across multiple platforms:

graph TB
    subgraph "Inttrest AI Platform"
        A[Vercel AI SDK] --> B[MCP Client Manager]
        B --> C[Event Aggregation Engine]
        C --> D[AI Analysis & Recommendations]
        D --> E[User Interface]
    end

    subgraph "MCP Server Ecosystem"
        F[Eventbrite MCP] --> B
        G[Instagram MCP] --> B
        H[LinkedIn MCP] --> B
        I[Meetup MCP] --> B
    end

    subgraph "Data Sources"
        J[Eventbrite API] --> F
        K[Instagram Web Scraping] --> G
        L[LinkedIn API/Scraping] --> H
        M[Meetup API] --> I
    end

    subgraph "User Experience"
        E --> N[Chat Interface]
        E --> O[Map Visualization]
        E --> P[Event Recommendations]
        E --> Q[Professional Networking]
    end

    style A fill:#e3f2fd
    style B fill:#e8f5e8
    style C fill:#fff3e0
    style D fill:#fce4ec

📋 Server Comparison

Server	Primary Focus	Data Source	Use Cases	Strengths
Eventbrite	Professional Events	Eventbrite API	Conferences, Workshops, Ticketed Events	Official API, Rich metadata, Payment info
Instagram	Social Events	Web Scraping + AI	Parties, Social gatherings, Trendy events	Visual content, Social buzz, Real-time discovery
LinkedIn	Business Events	LinkedIn API + Scraping	Networking, Professional development	Professional context, Industry insights, Quality networking
Meetup	Community Events	Meetup API	Local meetups, Interest groups, Recurring events	Community-driven, Group health analysis, Local focus

🛠️ Technical Architecture

Common MCP Framework

All servers follow the same architectural pattern:

// Standard MCP server structure
interface MCPServer {
  // Core server setup
  server: Server
  apiClient: APIClient
  contentAnalyzer: ContentAnalyzer

  // Tool handlers
  setupToolHandlers(): void

  // Lifecycle management
  start(): Promise<void>
  stop(): Promise<void>
}

Shared Components

1. Authentication Management

// OAuth 2.0 flow for API access
interface AuthConfig {
  clientId: string
  clientSecret: string
  redirectUri: string
  accessToken?: string
  refreshToken?: string
}

2. Rate Limiting

// Consistent rate limiting across all servers
interface RateLimitConfig {
  requests: number
  window: number // milliseconds
  burst?: number
  backoff?: 'exponential' | 'linear'
}

3. Caching Strategy

// Multi-tier caching for optimal performance
interface CacheConfig {
  redis?: RedisConfig     // Distributed cache
  memory: MemoryConfig    // Local cache
  ttl: number            // Time to live
}

4. AI Integration

// OpenAI integration for content analysis
interface AIAnalysisConfig {
  provider: 'openai' | 'anthropic'
  model: string
  temperature: number
  maxTokens: number
}

🔧 Installation & Setup

Quick Start

# Clone the repository
git clone https://github.com/FilippTrigub/inttrest
cd inttrest

# Install dependencies
npm install

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys

# Build all MCP servers
npm run build:mcp

# Start the platform
npm run dev

Environment Configuration

Create a comprehensive .env file:

# ===== EVENTBRITE MCP =====
EVENTBRITE_API_KEY=your_eventbrite_oauth_token
EVENTBRITE_API_BASE_URL=https://www.eventbriteapi.com/v3

# ===== INSTAGRAM MCP =====
INSTAGRAM_USERNAME=your_instagram_username
INSTAGRAM_PASSWORD=your_instagram_password
OPENAI_API_KEY=your_openai_api_key

# ===== LINKEDIN MCP =====
LINKEDIN_CLIENT_ID=your_linkedin_client_id
LINKEDIN_CLIENT_SECRET=your_linkedin_client_secret
LINKEDIN_ACCESS_TOKEN=your_linkedin_access_token

# ===== MEETUP MCP =====
MEETUP_API_KEY=your_meetup_api_key
MEETUP_CLIENT_ID=your_oauth_client_id
MEETUP_CLIENT_SECRET=your_oauth_client_secret

# ===== SHARED CONFIGURATION =====
# AI Analysis
OPENAI_API_KEY=your_openai_api_key
OPENAI_MODEL=gpt-4-turbo-preview

# Cache & Performance
REDIS_URL=redis://localhost:6379
CACHE_TTL=3600000
RATE_LIMIT_REQUESTS=100
RATE_LIMIT_WINDOW=3600000

# Logging
LOG_LEVEL=info
LOG_FILE=inttrest-mcp.log

# Geographic Defaults
DEFAULT_LOCATION=37.7749,-122.4194  # San Francisco
DEFAULT_RADIUS=25

📊 Usage Examples

Unified Event Search

// Search across all platforms simultaneously
async function searchAllPlatforms(query: string, location: string) {
  const mcpClient = await setupMCPClients()

  const [eventbriteEvents, instagramEvents, linkedinEvents, meetupEvents] = 
    await Promise.all([
      mcpClient.eventbrite.callTool({
        name: 'search_events',
        arguments: { q: query, location }
      }),

      mcpClient.instagram.callTool({
        name: 'search_instagram_events',
        arguments: { hashtags: [query], location }
      }),

      mcpClient.linkedin.callTool({
        name: 'search_professional_events',
        arguments: { keywords: [query], location }
      }),

      mcpClient.meetup.callTool({
        name: 'search_meetup_events',
        arguments: { keywords: [query], location }
      })
    ])

  return {
    professional: [...eventbriteEvents._meta.events, ...linkedinEvents._meta.events],
    social: instagramEvents._meta.events,
    community: meetupEvents._meta.events,
    total: eventbriteEvents._meta.events.length + 
           instagramEvents._meta.events.length + 
           linkedinEvents._meta.events.length + 
           meetupEvents._meta.events.length
  }
}

AI-Powered Event Analysis

// Cross-platform event analysis with AI
async function analyzeEventLandscape(location: string, interests: string[]) {
  const events = await searchAllPlatforms(interests.join(' '), location)

  const analysis = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    messages: [{
      role: 'system',
      content: 'Analyze the event landscape and provide insights about trends, opportunities, and recommendations.'
    }, {
      role: 'user',
      content: `
Analyze these events in ${location}:
- Professional Events: ${events.professional.length}
- Social Events: ${events.social.length}  
- Community Events: ${events.community.length}

User interests: ${interests.join(', ')}

Provide insights about:
1. Event trends in this location
2. Best networking opportunities
3. Personalized recommendations
4. Optimal timing for attendance
`
    }]
  })

  return {
    events,
    insights: analysis.choices[0].message.content,
    recommendations: await generatePersonalizedRecommendations(events, interests)
  }
}

Real-time Event Monitoring

// Set up monitoring across all platforms
async function setupEventMonitoring(userPreferences: UserPreferences) {
  const mcpClients = await setupMCPClients()

  // Monitor professional events
  await mcpClients.eventbrite.callTool({
    name: 'monitor_events',
    arguments: {
      categories: userPreferences.professionalInterests,
      location: userPreferences.location
    }
  })

  // Monitor social events  
  await mcpClients.instagram.callTool({
    name: 'monitor_instagram_accounts',
    arguments: {
      usernames: userPreferences.followedAccounts,
      keywords: userPreferences.socialInterests
    }
  })

  // Monitor networking events
  await mcpClients.linkedin.callTool({
    name: 'monitor_professional_networks', 
    arguments: {
      companies: userPreferences.companiesOfInterest,
      industries: userPreferences.industries
    }
  })

  // Monitor community events
  await mcpClients.meetup.callTool({
    name: 'monitor_group_activities',
    arguments: {
      groups: userPreferences.meetupGroups,
      notification_types: ['new_events', 'event_updates']
    }
  })
}

🧪 Testing Strategy

Comprehensive Test Suite

// Cross-platform integration tests
describe('MCP Server Integration', () => {
  test('all servers start successfully', async () => {
    const servers = await Promise.all([
      startEventbriteMCP(),
      startInstagramMCP(), 
      startLinkedInMCP(),
      startMeetupMCP()
    ])

    servers.forEach(server => {
      expect(server.status).toBe('running')
    })
  })

  test('unified event search works', async () => {
    const results = await searchAllPlatforms('tech conference', 'San Francisco')

    expect(results.total).toBeGreaterThan(0)
    expect(results.professional.length).toBeGreaterThan(0)
    expect(results.community.length).toBeGreaterThan(0)
  })

  test('AI analysis provides meaningful insights', async () => {
    const analysis = await analyzeEventLandscape('New York', ['technology', 'networking'])

    expect(analysis.insights).toBeDefined()
    expect(analysis.recommendations.length).toBeGreaterThan(0)
  })
})

Load Testing

# Test MCP server performance under load
npm run test:load -- --servers=all --concurrent=50 --duration=60s

📈 Performance Optimization

Caching Hierarchy

graph TD
    A[Client Request] --> B{Memory Cache}
    B -->|Hit| C[Return Cached Data]
    B -->|Miss| D{Redis Cache}
    D -->|Hit| E[Update Memory & Return]
    D -->|Miss| F[MCP Server Call]
    F --> G[API/Scraping]
    G --> H[Cache in Redis & Memory]
    H --> I[Return Fresh Data]

Performance Metrics

// Monitor performance across all servers
interface PerformanceMetrics {
  eventbrite: {
    avgResponseTime: number
    successRate: number
    cacheHitRate: number
  }
  instagram: {
    avgResponseTime: number
    successRate: number
    scrapingSuccessRate: number
  }
  linkedin: {
    avgResponseTime: number
    successRate: number
    apiQuotaUsage: number
  }
  meetup: {
    avgResponseTime: number
    successRate: number
    communityAnalysisTime: number
  }
}

🔒 Security & Privacy

Data Protection

// Consistent data anonymization
interface PrivacyConfig {
  anonymizeUserData: boolean
  retentionPeriodDays: number
  encryptSensitiveData: boolean
  auditLogging: boolean
}

// GDPR compliance
function anonymizeEventData(event: any): any {
  return {
    ...event,
    attendees: event.attendees?.map(anonymizeUser),
    organizer: anonymizeUser(event.organizer),
    sensitiveFields: undefined
  }
}

Rate Limiting & Abuse Prevention

// Multi-layered rate limiting
const rateLimits = {
  perUser: { requests: 100, window: 3600000 },
  perIP: { requests: 1000, window: 3600000 },
  perServer: { requests: 10000, window: 3600000 }
}

🚀 Deployment

Docker Configuration

# docker-compose.yml
version: '3.8'
services:
  inttrest-app:
    build: .
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=production
    depends_on:
      - redis
      - postgres

  eventbrite-mcp:
    build: ./mcp_servers/eventbrite-mcp
    environment:
      - EVENTBRITE_API_KEY=${EVENTBRITE_API_KEY}

  instagram-mcp:
    build: ./mcp_servers/instagram-server-next-mcp
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}

  linkedin-mcp:
    build: ./mcp_servers/linkedin-mcp-server
    environment:
      - LINKEDIN_CLIENT_ID=${LINKEDIN_CLIENT_ID}

  meetup-mcp:
    build: ./mcp_servers/mcp-meetup
    environment:
      - MEETUP_API_KEY=${MEETUP_API_KEY}

  redis:
    image: redis:7-alpine

  postgres:
    image: postgres:15-alpine
    environment:
      - POSTGRES_DB=inttrest

Production Monitoring

// Health checks for all MCP servers
async function healthCheck(): Promise<SystemHealth> {
  const checks = await Promise.allSettled([
    checkEventbriteHealth(),
    checkInstagramHealth(),
    checkLinkedInHealth(),
    checkMeetupHealth()
  ])

  return {
    overall: checks.every(check => check.status === 'fulfilled') ? 'healthy' : 'degraded',
    servers: {
      eventbrite: checks[0].status,
      instagram: checks[1].status,
      linkedin: checks[2].status,
      meetup: checks[3].status
    },
    timestamp: new Date().toISOString()
  }
}

📚 Additional Resources

• Eventbrite MCP Documentation
• Instagram MCP Documentation
• LinkedIn MCP Documentation
• Meetup MCP Documentation
• Vercel AI SDK Integration
• Architecture Overview

This comprehensive MCP server ecosystem powers the Inttrest platform's AI-driven event discovery capabilities across multiple platforms! 🚀