📌 1.Introduction

🤖 Agentic Sales Bot: MCP-Powered Lead Gen & Outreach System

A full-stack, autonomous sales pipeline built to satisfy the MCP-Powered Lead Gen + Outreach take-home assignment. It uses the Model Context Protocol (MCP) to expose tools, n8n as the agentic orchestrator, and Streamlit for real-time monitoring.

📋 Assignment Compliance Matrix

🏗️ System Architecture

The system follows the required Micro-Tool Architecture where n8n acts as the "Brain" (Agent) and Python acts as the "Hands" (Tools).

graph TD
    User["User / Dashboard"] -->|Trigger| n8n["n8n Orchestrator (Agent)"]
    n8n -->|HTTP Request| API["MCP Server (FastAPI)"]
    
    subgraph "MCP Tools (Python)"
    API -->|Tool| Gen[Lead Generator]
    API -->|Tool| Enrich["Lead Enricher (Groq/Rules)"]
    API -->|Tool| Draft[Message Drafter]
    API -->|Tool| Send[Outreach Sender]
    end
    
    Gen --> DB[("SQLite Database")]
    Enrich --> DB
    Draft --> DB
    Send --> DB
    DB --> User

📁 2.Project Structure

The project follows a clean micro-service architecture, separating backend services, agent tools, automation workflows, and configuration.

MCP-Powered Lead Gen+Enrichment+Outreach System/
│
├── app/                        # Main Application Source Code
│   ├── api.py                  # MCP Server (FastAPI) - The entry point
│   ├── dashboard.py            # Streamlit Frontend - The monitoring UI
│   ├── database.py             # SQLite Connection Manager
│   ├── generate_leads.py       # Tool: Generates dummy leads (Faker)
│   ├── enrich_leads.py         # Tool: Enriches leads (Groq LLM / Rules)
│   ├── generate_messages.py    # Tool: Drafts emails (LLM)
│   ├── send_messages.py        # Tool: Sends emails (SMTP)
│   └── mock_server.py          # SMTP Simulator for local testing
│
├── n8n/
│   └── pipeline_workflow.json  # n8n Workflow Export File
│
├── requirements.txt            # Python Dependencies
├── .env.example                # Configuration Example
└── README.md                   # Project Documentation

🧰 3.Tech Stack & Free Resources

Per assignment constraints, zero paid tools were used.

⚙️ 4.Installation & Setup

Prerequisites

• Python 3.8+ installed

• Docker Desktop installed (for n8n)

Step 1: Clone the Repository

git clone https://github.com/ARISTOTILE-GIT/MCP-Powered-Lead-Gen-Enrichment-Outreach-System-.git
cd MCP-Powered-Lead-Gen-Enrichment-Outreach-System-

Step 2: Install Dependencies

pip install -r requirements.txt

Step 3: Setup Environment

Create a .env file in the root directory and add your Groq API Key:

GROQ_API_KEY=gsk_your_actual_api_key_here

▶️ 5.How to Run the System

To see the full pipeline in action, you need 3 terminal windows running simultaneously.

Terminal 1: Mock SMTP Server

Catches emails locally to ensure safe testing (Dry/Live modes).

python app/mock_server.py

Terminal 2: Backend MCP Server

Exposes the tools (generate, enrich, send) via HTTP endpoints.

python app/api.py

Server starts at http://localhost:8000

Terminal 3: Frontend Dashboard

Monitors the pipeline progress and logs.

streamlit run app/dashboard.py

Dashboard opens at http://localhost:8501

🔄 6.Orchestration: n8n Workflow

The orchestration logic is handled by n8n, fulfilling the "Agent" requirement.

1. Start n8n (Docker)

docker run -it --rm --name n8n -p 5678:5678 --add-host=host.docker.internal:host-gateway n8nio/n8n:latest

2. Import Workflow:

• Open http://localhost:5678.

• *Click "Add Workflow" -> "Import from File".*

• Select n8n/sales_pipeline_workflow.json (included in this repo).

3. Execute:

• Click "Execute Workflow" to trigger the agentic loop.

📘 7.Usage Guide (Modes)

The dashboard allows you to control the "Intelligence" and "Safety" of the pipeline via interactive toggles.

🎛️ Sending Mode: Dry Run vs Live Run

🔹 Dry Run (Test Only)

• Simulates the sending process

• Logs generated content to the database/dashboard

• Does NOT interact with the SMTP server

• Status updates to: SENT_DRY_RUN

🔹 Live Run (Send Emails)

• Actually connects to the Mock SMTP server

• Dispatches real emails locally

• Status updates to: SENT

🧠 Enrichment Mode: AI vs Offline

🔹 Offline (Rules – Fast)

• Uses heuristic rules based on Industry / Role

• Assigns standard pain points

• Extremely fast

• Ideal for high-volume testing

🔹 AI Agent (Groq LLM)

• Calls the Groq API (Llama-3 model)

• Deeply analyzes the lead persona

• Generates hyper-personalized pain points and buying triggers

• Slower but significantly higher quality output

📝 Message Generation: AI vs Template

🔹 AI Generation

• Used when AI Agent enrichment is enabled

• Drafts unique emails using AI-generated pain points

• Powered by Llama-3

🔹 Template Fallback

• Used when Offline enrichment is selected

• Uses structured templates

• Ensures message coherence without requiring an LLM call

🌟 8.Bonus Features Implemented

Beyond the core requirements, the following "Nice-to-Have" features were added:

💾 CSV Export

• Download all generated leads

• Export message logs directly from the dashboard

• Useful for external analysis and reporting

🧹 Log Management

• Clear the database directly from the UI

• Wipe application log files

• No need to restart backend or dashboard servers

📉 Funnel Analytics

• Visual conversion funnel chart

• Tracks pipeline flow:

  • New → Enriched → Messaged → Sent

• Helps identify drop-offs and bottlenecks

📜 Live Logs

• Real-time log viewer embedded in the dashboard

• Observe AI-generated content as it happens

• Useful for debugging and transparency

📈 9.Output & Artifacts

1. Dashboard Monitoring

The Streamlit dashboard provides a comprehensive view of the pipeline health.

2. Sample Lead Data (JSON)

{
  "id": "lead_550e8400-e29b-41d4-a716-446655440000",
  "created_at": "2023-10-27T10:00:00Z",
  "status": "SENT",
  
  "basic_info": {
    "full_name": "Sarah Connor",
    "role": "Chief Technology Officer",
    "company_name": "SkyNet Systems",
    "industry": "Technology",
    "email": "sarah.connor@skynetsystems.com",
    "linkedin_url": "https://www.linkedin.com/in/sarah-connor-tech",
    "website": "https://www.skynetsystems.com",
    "country": "United States"
  },

  "enrichment_data": {
    "company_size": "Enterprise (1000+ employees)",
    "persona_tag": "Technical Decision Maker",
    "confidence_score": 92,
    "pain_points": [
      "Struggling with high cloud infrastructure costs and AWS bill shock.",
      "Technical debt slowing down new feature release cycles.",
      "Difficulty hiring and retaining senior DevOps engineers."
    ],
    "buying_triggers": [
      "Recently raised Series C funding.",
      "Posted 5 new job openings for 'Cloud Architect' last week."
    ]
  },

  "generated_outreach": {
    "email_subject": "Scaling SkyNet's tech without the cloud cost bloat",
    "email_body": "Hi Sarah,\n\nI noticed SkyNet Systems is scaling rapidly after your recent Series C. Congrats! As a CTO, balancing speed with spiraling cloud costs is often the biggest headache.\n\nOur AI-driven infrastructure tool helps engineering leaders like you slash AWS bills by 20% while automating technical debt reduction. \n\nWorth a 15-minute chat to see how we can optimize your roadmap?",
    "linkedin_dm": "Hi Sarah, saw SkyNet's growth. Impressive! If cloud costs or tech debt are slowing down your new releases, our AI tool helps CTOs reclaim engineering time. Open to a quick chat?",
    "sent_at": "2023-10-27T10:05:30Z"
  }
}

3. Sample Generated Email

Subject: Scaling Tech Debt at SkyNet Systems?

Hi Sarah,
I noticed SkyNet is scaling rapidly. As a CTO, dealing with cloud costs and technical debt usually becomes a bottleneck. 
Our automated optimization tool helps engineering leaders reclaim 20% of their roadmap...

🪪 10.License

This project is created for the Agentic AI Internship Technical Assessment.