Lead Scoring AI

By MEOK AI Labs — B2B lead scoring with engagement tracking and prioritization

Installation

pip install lead-scoring-ai-mcp

Usage

python server.py

Tools

score_lead

Score a lead based on firmographic data (company size, budget, engagement, decision maker contact).

Parameters:

• lead_id (str): Lead identifier
• company_size (int): Number of employees
• budget (float): Budget amount
• engagement_score (float): Engagement score 0-100
• has_decision_maker_contact (bool): Whether decision maker is known
• email_verified (bool): Whether email is verified
• website_traffic_monthly (int): Monthly website visitors

add_lead

Add a new lead to tracking.

Parameters:

• lead_id (str): Lead identifier
• company_name (str): Company name
• contact_name (str): Contact person name
• contact_email (str): Contact email
• company_size (int): Number of employees
• industry (str): Industry sector

update_lead_activity

Record lead activity (email_open, email_click, website_visit, demo_request, pricing_page, proposal_view, meeting_booked).

Parameters:

• lead_id (str): Lead identifier
• activity_type (str): Type of activity
• metadata (dict): Additional activity metadata

get_lead_score

Get current score for a lead.

get_all_leads

Get all leads with scores, optionally filtered by priority (hot, warm, cold).

get_lead_activities

Get activity history for a lead within a date range.

get_lead_timeline

Get engagement timeline for a lead.

predict_conversion

Predict conversion probability based on score and activity history.

get_priority_leads

Get leads above a minimum score threshold.

track_engagement_trend

Get engagement trend over time (increasing, decreasing, no_activity).

Authentication

Free tier: 15 calls/day. Upgrade at meok.ai/pricing for unlimited access.

License

MIT — MEOK AI Labs