Inbound Lead Generation

🚀 Lead Generation Server Documentation

This project offers a comprehensive lead generation system that combines various tools and services to automate the generation and enrichment of potential customers. It leverages technologies such as natural language processing (NLP), web crawling, API calls, and distributed computing.

📑 Table of Contents

1. Overview
2. Features
3. Architecture
4. Configuration
5. API Documentation
6. Examples
7. Advanced Configuration
8. Troubleshooting
9. Contribution
10. License
11. Roadmap
12. Support

📋 Overview

This project provides a complete lead generation system that integrates multiple tools and services to automate the generation and enrichment of potential customers. The system utilizes technologies such as natural language processing (NLP), web crawling, API calls, and distributed computing.

✨ Main Features

• Automatic Lead Generation: Automatically generate potential customers through keyword search and content analysis.
• Data Enrichment: Use various third - party services (e.g., Clearbit, Hunter.io) to supplement detailed information about leads.
• Status Monitoring: Track the status and processing progress of leads in real - time.
• Distributed Architecture: Support large - scale parallel processing and high scalability.

🧩 Core Components

1. Search Engine: Used for keyword matching and content scraping.
2. Data Enrichment Module: Integrate multiple third - party APIs for data supplementation.
3. Status Monitoring System: Track the entire lifecycle of leads.
4. Distributed Computing Framework: Support large - scale data processing and task distribution.

🏗️ Architecture

graph TD
    A[User Request] --> B(Front - end Service)
    B --> C[API Gateway]
    C --> D[Service Discovery]
    D --> E[Lead Generation Service]
    D --> F[Data Enrichment Service]
    D --> G[Status Monitoring Service]
    E --> H[Search Engine]
    F --> I[Third - party API]
    G --> J[Database]

• Front - end Service: Handle user requests and parse input parameters.
• API Gateway: Responsible for routing and traffic management.
• Service Discovery: Implement communication and coordination between services.
• Lead Generation Service: Perform keyword search and content scraping.
• Data Enrichment Service: Call third - party APIs for data supplementation.
• Status Monitoring Service: Track the status and processing progress of leads.

⚙️ Configuration

🌐 Environment Variables

export SEARCH_ENGINE_API_KEY=your_api_key
export THIRD_PARTY_API_SECRET=your_secret

📄 Default Configuration File

# config.py
class Configuration:
    def __init__(self):
        self.search_engine = SearchEngineConfig()
        self.data_enrichment = DataEnrichmentConfig()
        self.monitoring = MonitoringConfig()

    # Other methods for loading and parsing configuration parameters

📄 API Documentation

1. Generate Lead

POST /tools/lead_generation
Content-Type: application/json

{
  "search_terms": "OpenAI"
}

Response:

{
  "lead_id": "550e8400-e29b-41d4-a716-446655440000",
  "status": "pending",
  "estimated_time": 15
}

2. Enrich Data

POST /tools/data_enrichment
Content-Type: application/json

{
  "lead_id": "550e8400-e29b-41d4-a716-446655440000"
}

3. Monitor Status

GET /tools/lead_maintenance

💻 Examples

🐍 Python Client

from mcp.client import Client

async with Client() as client:
    # Generate lead
    lead = await client.call_tool(
        "lead_generation",
        {"search_terms": "Anthropic"}
    )
    
    # Enrich data
    enriched = await client.call_tool(
        "data_enrichment",
        {"lead_id": lead['lead_id']}
    )
    
    # Get status
    status = await client.call_tool(
        "lead_status",