SocialAnalytics-MCP-rapidapi by lama-assaf - MCP Server

Social Media Scraper - RapidAPI MCP Server

A comprehensive Model Context Protocol (MCP) server that provides social media scraping capabilities for LinkedIn, Facebook, Instagram, and Google search functionality.

What is MCP?

Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect with external data sources and tools. MCP servers act as bridges between AI models and various services, allowing for enhanced capabilities like real-time data access, API integrations, and custom tool execution.

Features

This server exposes comprehensive social media analytics tools for AI assistants:

LinkedIn Analytics (16 Tools)

Profile Analytics: Personal and company profile data with engagement metrics
Content Analytics: Posts, comments, reactions, and engagement analysis
Network Analytics: Company posts, job market insights, and search capabilities
Performance Tracking: Profile reactions, post reposts, and interaction patterns

Facebook Analytics

Profile Intelligence: Public profile data with follower insights
Engagement Metrics: Activity patterns and audience interaction data
Content Analysis: Profile engagement and reach analytics

Instagram Analytics

Profile Performance: Comprehensive profile data with follower demographics
Growth Analytics: Follower count trends and engagement rates
Content Insights: Profile activity and audience interaction patterns

Web Intelligence

Google Search: Advanced web searches with location and pagination support
Competitive Analysis: Search competitor mentions and market trends

Installation

Step 1: Clone or Download Project

Clone this repository or download the project files:

git clone <repository-url>
cd SocialAnalytics-MCP-rapidapi

Or download and extract the ZIP file to your desired location.

Step 2: Install Dependencies

Install all required dependencies using uv:

uv sync

This will install all dependencies listed in pyproject.toml:

fastmcp>=2.11.0
httpx>=0.28.1
mcp[cli]>=1.9.4
python-dotenv>=1.1.0

Step 3: Create Environment File

Create a .env file in the project root to store your API keys:

touch .env

Environment Configuration

Step 1: Get API Keys

RapidAPI Key

Sign up at RapidAPI
Subscribe to the following APIs:
- LinkedIn Scraper API: https://rapidapi.com/karimgreek/api/linkedin-scraper-api-real-time-fast-affordable
- Facebook Scraper API: https://rapidapi.com/krasnoludkolo/api/facebook-scraper3
- Instagram Scraper API: https://rapidapi.com/thetechguy32744/api/instagram-scraper-stable-api
Copy your RapidAPI key from the dashboard

Google Serper API Key

Sign up at Serper.dev
Get your API key from the dashboard

Step 2: Configure Environment Variables

Add your API keys to the .env file you created:

RAPIDAPI_KEY=your_rapidapi_key_here
SERPER_API_KEY=your_serper_api_key_here

Important: Never commit your .env file to version control. It's already included in .gitignore.

Usage

Running with Claude Desktop

Step 1: Install the Server

You can install this server in Claude Desktop and interact with it right away by running:

uv run mcp install main.py

Step 2: Verify Installation

Later, go to Claude AI (desktop version) and you will see changes in the platform similar to the screenshot shown.

Step 3: Start Using the Tools

Paste the URLs of required platform and ask the AI to provide information of the mentioned URLs.

Analytics Use Cases & Examples

🔍 Competitive Intelligence

Analyze competitor engagement: Get all posts and reactions for https://linkedin.com/company/competitor-name

Track competitor hiring: Search jobs posted by https://linkedin.com/company/competitor-name

📊 Content Performance Analysis

Analyze top-performing content: Get posts and engagement data for https://linkedin.com/in/industry-leader

Monitor brand mentions: Search LinkedIn posts containing "your-brand-name"

👥 Audience Insights

Profile audience analysis: Get follower demographics for https://facebook.com/your-company-page

Influencer analysis: Get Instagram profile metrics for @influencer-username

📈 Social Media Monitoring

Track brand sentiment: Get recent posts and comments mentioning "your-company"

Monitor industry trends: Search posts with keywords "artificial intelligence trends 2024"

🎯 Lead Generation

Identify prospects: Get company employee profiles from https://linkedin.com/company/target-company

Research decision makers: Get profile details for https://linkedin.com/in/ceo-name

📱 Cross-Platform Analytics

Compare social presence: Analyze Instagram @brand vs Facebook /brand-page engagement

Content strategy insights: Track post performance across LinkedIn company page and executive profiles

Troubleshooting

MCP Installation Issues

If the MCP tools don't appear in Claude Desktop:

Step 1: End Claude Processes

Windows: Open Task Manager (Ctrl+Shift+Esc)
Mac: Open Activity Monitor
End all Claude-related processes

Step 2: Reinstall the Server

uv run mcp install main.py

Step 3: Restart Claude Desktop

Paste the URLs of required platform and ask the AI to provide information of the mentioned URLs.

Analytics Data Collection Issues

Rate Limiting

If you encounter rate limiting errors:

Wait 60 seconds between requests for the same platform
Use different API endpoints to distribute load
Consider upgrading RapidAPI subscription for higher limits

Incomplete Data Returns

For missing analytics data:

Verify profile/page is public and accessible
Check if account has privacy restrictions
Some metrics may require business/creator account access

Facebook/Instagram Access Issues

Ensure profiles are public business pages
Personal profiles may have limited data availability
Some engagement metrics require page admin access

LinkedIn Premium Data

Advanced analytics may require LinkedIn Premium URLs
Company data depends on public visibility settings
Employee lists limited by company privacy settings

Testing with MCP Inspector

For development and debugging:

uv run mcp dev main.py

API Key Validation

Test your API keys independently:

# Test RapidAPI connection
curl -H "x-rapidapi-key: YOUR_KEY" \
     -H "x-rapidapi-host: linkedin-scraper-api-real-time-fast-affordable.p.rapidapi.com" \
     "https://linkedin-scraper-api-real-time-fast-affordable.p.rapidapi.com/profile/detail?username=test"

# Test Serper API
curl -X POST "https://google.serper.dev/search" \
     -H "X-API-KEY: YOUR_SERPER_KEY" \
     -d '{"q":"test query"}'

Project Structure

SocialAnalytics-MCP-rapidapi/
├── docs/                 # API endpoint documentation
│   ├── LinkedIn_Scraper_API_endpoints.md
│   ├── Facebook_api_endpoints.md
│   ├── Instagram_api_enpoints.md
│   └── (not-used)FreshLinkedInProfile_api_endpoints.md
├── CLAUDE.md             # Claude Code instructions for this project
├── Claude_View.png       # Screenshot of Claude Desktop integration
├── main.py               # Main MCP server implementation (583 lines)
├── memories.json         # MCP memory storage
├── pyproject.toml        # Project configuration and dependencies
├── README.md             # Project documentation
├── uv.lock               # UV lock file for reproducible builds
└── .env                  # Environment variables (API keys) - CREATE THIS

Analytics Data Examples

LinkedIn Profile Response (Filtered)

{
  "name": "John Doe",
  "headline": "Senior Software Engineer at TechCorp | AI & ML Specialist",
  "location": "San Francisco, CA",
  "connections": "500+",
  "followers": "1,234",
  "current_role": {
    "title": "Senior Software Engineer",
    "company": "TechCorp",
    "duration": "2 years 3 months"
  },
  "education": {
    "school": "Stanford University",
    "degree": "BS Computer Science"
  },
  "top_skills": ["Python", "Machine Learning", "AWS", "React", "SQL"]
}

Facebook Profile Response (Filtered)

{
  "name": "Tech Company Inc",
  "likes": "98,765",
  "followers": "125,432",
  "about": "Leading technology solutions provider specializing in AI and cloud infrastructure. Founded in 2018, we serve enterprise clients worldwide with cutting-edge software solutions...",
  "category": "Technology Company",
  "location": "San Francisco, CA",
  "page_metrics": {
    "checkins": 1243,
    "rating": 4.8,
    "review_count": 567
  }
}

Instagram Profile Response (Filtered)

{
  "username": "tech_influencer",
  "full_name": "Tech Innovator",
  "biography": "AI & Tech Trends | 100K+ followers | Sharing the latest in technology and innovation | Speaker & Consultant",
  "followers": 156789,
  "following": 892,
  "posts_count": 1247,
  "is_verified": true,
  "engagement_rate": "4.2%",
  "avg_likes": 6543
}

LinkedIn Posts Response (Filtered - Max 5 Posts)

{
  "posts": [
    {
      "content": "Excited to share our Q4 results! Our AI platform has processed over 10M transactions this quarter, helping businesses reduce costs by 30%. The future of automation is here and we're leading the charge...",
      "likes": 142,
      "comments": 23,
      "shares": 12,
      "reactions": {"like": 100, "celebrate": 42},
      "date": "2024-01-15",
      "author": {
        "name": "John Doe",
        "title": "CEO at TechCorp"
      }
    }
  ],
  "total_posts_available": 47,
  "posts_returned": 5
}

All data is returned as readable JSON when using Claude Desktop for easy analysis and insights extraction.

Context Window Optimization

To prevent MCP context window overload, this server implements intelligent data filtering:

Response Size Controls

LinkedIn Profiles: Limited to ~6KB of essential data (name, title, connections, current role)
LinkedIn Posts: Maximum 5 posts per request, ~5KB limit with key engagement metrics
Facebook/Instagram: Limited to ~4KB focusing on follower counts and engagement rates
Search Results: Maximum 8 results per query with truncated content

Data Filtering Strategy

Excluded Fields: Profile images, media URLs, full work histories, nested user objects
Text Truncation: Long descriptions limited to 300-500 characters
List Limits: Maximum 10 items in any array (comments, posts, connections)
Essential Focus: Only analytics-relevant metrics and engagement data retained

Customization

Modify response limits in main.py:

RESPONSE_LIMITS = {
    "linkedin_profile": 6000,    # Characters
    "linkedin_posts": 5000,      # Characters  
    "max_posts_returned": 5,     # Number of posts
    "max_comments_returned": 10  # Number of comments
}

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

In case of any queries, please leave a message or contact me via the email provided in my profile.

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