MCP Server Examples

Found 15 marketing managers at tech companies in San Francisco:

1. Sarah Chen - Senior Marketing Manager at Stripe
   - 8+ years experience in B2B SaaS marketing
   - Recent post: "The future of payment technology in 2024"
   - Profile: https://linkedin.com/in/sarahchen-marketing
   
2. Michael Rodriguez - VP Marketing at Airbnb
   - Expert in growth marketing and user acquisition
   - Recent post: "Building community-driven marketing campaigns"
   - Profile: https://linkedin.com/in/mrodriguez-growth

[... additional results ...]

Competitive Analysis - Week of [Date]

LinkedIn Activity:
• Competitor A (TechCorp): 12 posts, avg 240 engagements
  - Top post: "AI transformation in enterprise" (450 likes)
  - Content themes: Product updates (40%), Thought leadership (60%)

• Competitor B (DataFlow): 8 posts, avg 180 engagements  
  - Top post: "New partnership announcement" (320 likes)
  - Content themes: Partnerships (50%), Case studies (50%)

Instagram Activity:
• Competitor A: 6 posts, avg 890 likes
  - Behind-the-scenes content performing well
  - Story highlights: Product demos, Team culture

• Competitor B: 4 posts, avg 650 likes
  - Customer success stories driving engagement
  - Heavy use of carousel posts

// Suggested improvements based on research:

interface RateLimitConfig {
  // Standard headers (found in 85% of APIs researched)
  headers: {
    'X-RateLimit-Limit': number;
    'X-RateLimit-Remaining': number;
    'X-RateLimit-Reset': number;
    'X-RateLimit-Window': string; // e.g., "1hour", "15min"
  };
  
  // Progressive backoff (used by Stripe, GitHub, Twitter)
  backoffStrategy: {
    initial: number;    // 1000ms
    multiplier: number; // 2.0
    maximum: number;    // 30000ms
  };
  
  // Burst allowance (found in LinkedIn, Reddit APIs)
  burst: {
    enabled: boolean;
    limit: number;
    window: string;
  };
}

AI Coding Assistant Discussions - Last 7 Days

Reddit Analysis:
• r/programming (234 mentions)
  - Top thread: "GitHub Copilot vs ChatGPT for coding" (1.2k upvotes)
  - Sentiment: 60% positive, 25% neutral, 15% negative
  - Key concerns: Code quality, learning impact, pricing

• r/MachineLearning (89 mentions)  
  - Focus on code generation models
  - Discussion about fine-tuning for specific languages

Twitter Analysis:
• 1,247 tweets mentioning "AI coding assistant"
• Top influencers: @programmer_dev, @ml_engineer, @tech_lead
• Trending topics: #CodeGeneration, #DeveloperProductivity
• Sentiment trend: Increasingly positive over the week

{
  "dashboardConfig": {
    "updateFrequency": "daily",
    "sources": [
      "linkedin_company_updates",
      "twitter_competitor_mentions", 
      "reddit_industry_discussions",
      "google_news_alerts"
    ],
    "competitors": [
      "Competitor A",
      "Competitor B", 
      "Competitor C"
    ],
    "keywords": [
      "market trends",
      "product launches",
      "funding news",
      "partnership announcements"
    ]
  }
}

async function robustApiCall(toolName: string, params: any) {
  const maxRetries = 3;
  let attempt = 0;
  
  while (attempt < maxRetries) {
    try {
      const result = await mcpServer.call(toolName, params);
      return result;
    } catch (error) {
      attempt++;
      
      if (error.code === 'RATE_LIMIT_EXCEEDED') {
        // Wait and retry
        const waitTime = Math.pow(2, attempt) * 1000; // Exponential backoff
        await new Promise(resolve => setTimeout(resolve, waitTime));
        continue;
      }
      
      if (error.code === 'INVALID_CREDENTIALS') {
        throw new Error('API credentials need to be updated');
      }
      
      if (attempt === maxRetries) {
        throw error;
      }
    }
  }
}

// ❌ Inefficient - Multiple individual calls
for (const user of users) {
  const profile = await client.linkedinUserProfile({ userId: user.id });
  profiles.push(profile);
}

// ✅ Efficient - Batch operation
const profiles = await client.linkedinBatchUserProfiles({
  userIds: users.map(u => u.id),
  batchSize: 10
});

const cache = new Map();
const CACHE_TTL = 3600000; // 1 hour

async function cachedApiCall(toolName, params) {
  const cacheKey = `${toolName}:${JSON.stringify(params)}`;
  const cached = cache.get(cacheKey);
  
  if (cached && Date.now() - cached.timestamp < CACHE_TTL) {
    return cached.data;
  }
  
  const result = await mcpServer.call(toolName, params);
  cache.set(cacheKey, { data: result, timestamp: Date.now() });
  
  return result;
}