🧠 AI-Driven Sales Prospect Assessment Utility

This lightweight Micro-Control Protocol (MCP) utility leverages advanced language models to systematically qualify incoming sales leads against the established BANT criteria (Budgetary capacity, Decision-making Authority, Underlying Need, Implementation Timeline). It meticulously prompts the user sequentially for each required data facet.

✨ Core Capabilities

• Intelligent Data Capture: Utilizes the LLM to interpret conversational input, extract necessary BANT parameters, and assign a quantitative qualification score.
• Guided Interaction: Enforces a strict turn-based progression, soliciting one specific piece of qualification information per exchange.
• Ephemeral State Storage: Leverages high-speed local memory for maintaining session context; adaptable for persistence layer upgrades (e.g., Redis).
• Platform Interoperability: Built-in support for standard MCP communication streams, specifically Server-Sent Events (sse), facilitating seamless linkage with Dify agents or Cursor environments.

🛠️ Deployment Prerequisites

Configuration requires setting your OpenAI access token within the environment configuration file (.env).

bash OPENAI_API_KEY=your-secret-api-key-here

Initiate the underlying NodeJS control service:

bash npm install npm start

(Optional) To expose the local service endpoint to external networks, utilize a tunneling solution like ngrok:

bash ngrok http 3001

Dify Orchestrator Configuration Example

When configuring the consuming agent/workflow in Dify, utilize these specifications to establish the communication channel:

{ "lead_qualification_stream": { "transport_mechanism": "sse", "remote_endpoint": "https:///sse", "request_metadata": {}, "connection_timeout_seconds": 50, "data_read_timeout_seconds": 50 } }

🧪 Operational Illustration

Invoked Utility Name: conversational-prospect-scrutiny-module Initial Data Payload:

{ "sessionId": "user_session_789", "message": "Our allocated funding for this initiative is capped at one thousand dollars." }

System Response:

{ "content": [ { "type": "text_response", "text": "To confirm the decision structure: Are you the final signatory for procurement, or does the assessment process involve additional stakeholders?" } ], "status": "awaiting_next_input", "error": false }

Internal State Snapshot (Post-Interaction):

{ "qualification_data": { "budget": "\$1000 monthly allocation", "authority": "", "need": "", "timeline": "" }, "scoring_matrix": { "budget": 30, "authority": 0, "need": 0, "timeline": 0 }, "cumulative_score": 30, "next_parameter_to_probe": "authority", "last_query_field": "authority", "last_query_text": "To confirm the decision structure: Are you the final signatory for procurement, or does the assessment process involve additional stakeholders?" }