Lead Qualifier MCP Tool

🤖 Lead Qualifier MCP Tool

A lightweight MCP tool that uses ChatGPT to qualify leads over BANT mechanism (Budget, Authority, Need, Timeline). And guide users to enter leads informations question by question.

🚀 Features

🧠 LLM-powered lead qualification info (BANT) extraction and scoring
💬 One field per turn, with conversational flow
💾 Fast as in-memory session tracking, can be extended to Redis
🔌 Compatible with Dify / Cursor via MCP (sse)

⚙️ Setup

Configure ChatGPT apikey in your .env file.

OPENAI_API_KEY=1234

Start your NodeJS server, which is your MCP server.

npm install
npm start

Optional: expose your server using ngrok

ngrok http 3001

Dify Agent Strategy Configuration

{
  "lead_qualification": {
    "transport": "sse",
    "url": "https://24c3-172-235-53-238.ngrok-free.app/sse",
    "headers": {},
    "timeout": 50,
    "sse_read_timeout": 50
  }
}

🛠 Example

Tool name: lead-qualifier
Input:

{
  "sessionId": "abc123",
  "message": "We have a budget of $1000"
}

Output:

{
  content: [
    {
      type: "text",
      text: "Are you the main person evaluating tools like this, or is there someone else involved in the decision?"
    }
  ],
  isError: false
}

Session:

{
  "qualificationMap": {
    "budget": "$1000 per month",
    "authority": "",
    "need": "",
    "timeline": ""
  },
  "scoreMap": {
    "budget": 30,
    "authority": 0,
    "need": 0,
    "timeline": 0
  },
  "totalScore": 30,
  "nextField": "authority",
  "lastPromptedField": "authority",
  "lastPromptedQuestion": "Are you the main person evaluating tools like this, or is there someone else involved in the decision?"
}

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A lightweight server that uses ChatGPT to qualify leads using the BANT framework (Budget, Authority, Need, Timeline) through a conversational question-by-question approach.

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