LinkedIn Profile Data Mining MCP Server
A comprehensive Model Context Protocol (MCP) server for LinkedIn profile data mining, search, and contact information enrichment. This server integrates all the powerful features from the original data mining tool into an MCP-compatible interface.
Features
🔍 Advanced Search Capabilities
- Google Search Integration: Uses Google Custom Search API for LinkedIn profile discovery
- AI-Powered Query Expansion: Generates additional search queries using OpenAI GPT-4o mini
- Smart Filtering: AI-based relevance filtering to ensure high-quality results
- Location-Based Search: Supports global location targeting for comprehensive coverage
📊 Profile Data Extraction
- Direct LinkedIn Scraping: Extracts profile data directly from LinkedIn pages
- Nubela Proxycurl Fallback: Uses Nubela API when direct scraping fails
- Structured Data Parsing: Extracts JSON-LD structured data from LinkedIn profiles
- Comprehensive Profile Fields: Name, company, job title, description, followers, etc.
📞 Contact Information Enrichment
- Apollo.io Integration: Enriches profiles with email addresses and phone numbers
- Company Information: Retrieves detailed company descriptions and contact details
- Professional Validation: Ensures contact information accuracy through API validation
🤖 AI-Powered Features
- Profile Summarization: Generates concise professional summaries using AI
- Relevance Scoring: AI-based filtering to match search intent
- Query Optimization: Intelligent search query generation and expansion
- Multiple LLM Support: OpenAI, Gemini, OpenRouter, and Ollama compatibility
💾 Data Management
- SQLite Database: Persistent storage for all extracted profiles
- CSV Export: Easy data export for analysis and CRM integration
- Duplicate Prevention: Automatic detection and prevention of duplicate profiles
- Data Validation: Ensures data quality and completeness
Installation
-
Clone or navigate to the server directory:
cd smithery-servers/profile-searcher -
Install dependencies:
npm install -
Configure API keys:
# Copy the example configuration file cp .env.example .env # Edit .env with your API keys nano .env # or use your preferred editor -
Start the development server:
npm run dev
🔑 API Keys Configuration
📋 See CONFIGURATION.md for detailed setup instructions
Quick Setup:
- Required: Apollo.io API key → Get from apollo.io/settings/integrations
- Required: OpenAI API key → Get from platform.openai.com/api-keys
- Optional: Nubela API key → Get from nubela.co/proxycurl
Environment Variables (.env file):
APOLLO_API_KEY=your_apollo_api_key_here
OPENAI_API_KEY=sk-your_openai_api_key_here
NUBELA_API_KEY=your_nubela_api_key_here
DEBUG=false
For Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"profile-searcher": {
"command": "node",
"args": ["/path/to/smithery-servers/profile-searcher/dist/index.js"],
"env": {
"APOLLO_API_KEY": "your_apollo_api_key_here",
"OPENAI_API_KEY": "sk-your_openai_api_key_here"
}
}
}
}
Available Tools
1. search_linkedin_profiles
Search for LinkedIn profiles based on keywords.
Parameters:
keywords(string): Search keywords (e.g., "AI podcast host")num_results(number): Number of results to return (default: 20)
Example:
{
"keywords": "AI podcast host",
"num_results": 10
}
2. extract_profile_data
Extract detailed profile data from LinkedIn URLs.
Parameters:
urls(array): Array of LinkedIn profile URLsinclude_contact_info(boolean): Whether to include contact info (default: true)
Example:
{
"urls": [
"https://www.linkedin.com/in/example-profile",
"https://www.linkedin.com/in/another-profile"
],
"include_contact_info": true
}
3. mine_linkedin_data
Comprehensive data mining: search, extract, and enrich profile data.
Parameters:
keywords(string): Keywords to search fornum_results(number): Number of profiles to process (default: 20)export_csv(boolean): Whether to export to CSV (default: true)csv_filename(string, optional): Custom CSV filename
Example:
{
"keywords": "blockchain developer",
"num_results": 25,
"export_csv": true,
"csv_filename": "blockchain_developers.csv"
}
4. get_contact_info
Get contact information for a specific person using Apollo API.
Parameters:
person_name(string): Full name of the personcompany_name(string): Company where the person works
Example:
{
"person_name": "John Smith",
"company_name": "Tech Corp"
}
5. export_to_csv
Export all stored profile data to CSV file.
Parameters:
filename(string, optional): Custom filename for export
Example:
{
"filename": "all_profiles_export.csv"
}
6. get_stored_profiles
Retrieve all profiles stored in the database.
Parameters: None
7. generate_search_queries
Generate additional search queries using AI.
Parameters:
main_query(string): Main search query to expandnum_queries(number): Number of additional queries (default: 3)
Example:
{
"main_query": "site:linkedin.com/in AI podcast host",
"num_queries": 5
}
Data Structure
Profile Data Fields
Each extracted profile contains the following fields:
interface ProfileData {
author_profile_url: string; // LinkedIn profile URL
author_name?: string; // Full name
authors_desc?: string; // Profile headline/description
Company?: string; // Current company
Job_title?: string; // Current job title
InteractionStatistic_followers?: string; // Follower count
email?: string; // Email address (from Apollo)
phone1?: string; // Primary phone (from Apollo)
phone2?: string; // Secondary phone (from Apollo)
about_company?: string; // Company description (from Apollo)
profile_summary?: string; // AI-generated summary
post_details?: string; // Recent post content
transcript?: string; // Podcast/video transcripts
post_summary?: string; // AI summary of posts
transcript_summary?: string; // AI summary of transcripts
author_activity?: string; // Activity summary
}
Database Schema
The server uses SQLite with three main tables:
author_urls_table: Stores complete profile informationvalidated_profiles: Tracks AI validation resultssearch_queries: Stores search queries and results
File Structure
smithery-servers/profile-searcher/
├── src/
│ └── index.ts # Main server implementation
├── package.json # Dependencies and scripts
├── README.md # This documentation
├── smithery.yaml # Smithery configuration
├── Database/ # SQLite database files
│ └── author_profile_.db # Main database
└── Data/ # CSV export files
└── *.csv # Exported profile data
Usage Examples
Basic Profile Search
// Search for AI podcast hosts
const result = await mcpClient.callTool("search_linkedin_profiles", {
keywords: "AI podcast host",
num_results: 15
});
Comprehensive Data Mining
// Mine data for blockchain developers
const result = await mcpClient.callTool("mine_linkedin_data", {
keywords: "blockchain developer",
num_results: 30,
export_csv: true,
csv_filename: "blockchain_talent.csv"
});
Contact Information Lookup
// Get contact info for a specific person
const result = await mcpClient.callTool("get_contact_info", {
person_name: "Jane Doe",
company_name: "AI Innovations Inc"
});
Rate Limiting and Best Practices
- Respect Rate Limits: The server includes built-in delays between requests
- API Key Management: Keep your API keys secure and monitor usage
- Data Privacy: Ensure compliance with data protection regulations
- Ethical Use: Use the tool responsibly and respect LinkedIn's terms of service
Troubleshooting
Common Issues
- Missing Dependencies: Run
npm installto ensure all packages are installed - API Key Errors: Verify all required API keys are correctly configured
- Database Permissions: Ensure write permissions for the Database directory
- Network Issues: Check internet connectivity for API calls
Debug Mode
Enable debug mode in configuration for detailed logging:
{
"debug": true
}
Contributing
This server is based on the comprehensive data mining tool and includes all its advanced features. For improvements or bug reports, please refer to the original implementation.
License
ISC License - See package.json for details.
Disclaimer
This tool is for legitimate business and research purposes. Users are responsible for complying with LinkedIn's terms of service, data protection regulations, and applicable laws. Always respect privacy and obtain necessary permissions before collecting personal data.