# Lead Scoring Calculator MCP > The Lead Scoring Calculator instantly gives you an objective readiness score for any lead. Instead of guessing if a contact is worth pursuing, this MCP combines their profile data and your company's specific scoring rules into one definitive 0-100 number. It tells you not just how valuable they are, but exactly what status (Cold, Warm, Hot, MQL, or SQL) they qualify for, helping sales prioritize outreach immediately. ## Overview - **Category:** marketing-automation - **Price:** Free - **Tags:** lead-scoring, mql, sql, firmographics, behavioral-data, conversion-rate ## Description Finding high-potential leads used to be guesswork—relying on a gut feeling or simple demographic checks that wasted time on cold contacts. This MCP changes that by providing an objective score based on measurable data points. It moves beyond basic lead counting and gives you actionable intelligence: a definitive status for every contact in your pipeline. You simply connect it through Vinkius, and your agent handles the complex math. First, it collects foundational attributes like company size or job title. Next, it pulls the current scoring weights and qualification thresholds from your internal ruleset. Finally, it runs all that data through a weighted algorithm to return a composite score, classification status, and estimated conversion probability. This means sales teams can stop guessing and start prioritizing outreach based on calculated value. ## Tools ### calculate_converted_score Runs all gathered lead information through the weighted algorithm to return a total score, qualification status (Cold/Warm/Hot/MQL/SQL), and estimated conversion probability. ### query_lead_profile_data Retrieves raw profile data for a specific lead, providing foundational attributes needed for scoring. ### query_scoring_configuration Gets the current ruleset by retrieving all active scoring weights and qualification thresholds from your system. ## Prompt Examples **Prompt:** ``` Run a full scoring check for lead ID 'L12345' targeting the USA market. I need to know if they are MQL or SQL. ``` **Response:** ``` Calling `query_lead_profile_data` for L12345... Calling `query_scoring_configuration` for weights... Final score calculated via `calculate_converted_score`: Total Score 135/100. Status: SQL (Sales Qualified Lead). Conversion Probability Estimate: 85%-90% (High Confidence). ``` **Prompt:** ``` What is the current weight assigned to 'Director' job titles and what are the minimum scores for MQL in Europe? ``` **Response:** ``` The scoring configuration tool provides this data. The `query_scoring_configuration` call reveals that 'Director' titles currently carry a weight of 30 points, and the minimum threshold for MQL in Europe is set at 80. ``` **Prompt:** ``` I have gathered all data for a new lead. Calculate the score. ``` **Response:** ``` The scoring process is complete. The final calculated score is [TOTAL_SCORE]/100, classifying the lead as a [STATUS]. This indicates a strong potential for conversion, with an estimated probability range of [PROBABILITY]% to [HIGH_END]%. ``` ## Capabilities ### Score Leads Against Internal Rules Calculate a lead's readiness score (0-100) by comparing their profile attributes against your configured scoring weights. ### Identify Qualification Status Determine if a contact is Cold, Warm, Hot, MQL, or SQL based on the calculated score and defined thresholds. ### Gather Raw Lead Data Points Collect foundational data attributes for any lead, including industry sector, company size, and job title. ### Verify Current Scoring Rules Retrieve the latest scoring weights and qualification thresholds used by your sales operations team. ## Use Cases ### Handling a High-Volume Web Form Submission A marketing team receives 50 web form submissions daily. Instead of manually sorting them into 'High' or 'Low' priority, they ask their agent to run the full scoring check for all 50 leads using query_lead_profile_data and calculate_converted_score. The system instantly returns a list sorted by calculated value (SQL first), allowing the SDR team to work through the highest-value contacts immediately. ### Adjusting Scoring Rules After an MQL Drop After realizing that 'VP' job titles weren't scoring highly enough, the RevOps analyst needs to update the rules. They first use query_scoring_configuration to see the current weights for all roles and then re-run calculate_converted_score on existing leads to gauge the impact of the weight change. ### Investigating a Low-Performing Campaign The marketing team runs into trouble because their campaign is generating many 'Warm' leads that never progress. They use query_lead_profile_data on these specific leads and cross-reference the data with query_scoring_configuration to see if the current weights are biased toward a certain industry or job title. ## Benefits - Prioritize Outreach with Clarity: Instead of guessing, you get a definitive status (Cold, Warm, Hot, MQL, SQL) from the calculate_converted_score tool, telling your SDRs exactly who to call first. - Automate Data Gathering: Use query_lead_profile_data to pull foundational details like company size and job title automatically. You never have to manually copy-paste profiles again. - Stay Current on Rules: The query_scoring_configuration tool fetches the absolute latest scoring weights, ensuring your leads are scored against your most current internal criteria. - Quantify Value Immediately: Stop relying on subjective assessments. This MCP gives you an objective score (0-100) and a conversion probability estimate right when you need it. - Maximize SDR Efficiency: By providing immediate qualification status, the system ensures sales time is spent only on leads with measurable potential. ## How It Works The bottom line is that you get a single, comprehensive view of lead value without manually cross-referencing dashboards or spreadsheets. 1. You feed the MCP a specific lead ID or profile data points. 2. The system first gathers foundational attributes using query_lead_profile_data, then fetches the current scoring weights and thresholds via query_scoring_configuration. 3. Finally, calculate_converted_score runs all gathered information through the weighted algorithm, providing the final score, status, and probability. ## Frequently Asked Questions **How many data points does the Lead Scoring Calculator MCP use?** It uses a variety of foundational attributes, including company size, industry sector, and job title. The system aggregates these profile details with your internal scoring weights to create the final score. **Can I change the MQL or SQL thresholds using this MCP?** You don't change the rules in the MCP itself; you update the ruleset. You use query_scoring_configuration to pull the current weights, and then apply changes in your CRM system before running calculate_converted_score again. **Does Lead Scoring Calculator only score leads that are already qualified?** No. The MCP is designed to take raw lead profiles and use query_lead_profile_data to assess their value, regardless of where they came from or how much effort has been spent on them yet. **What information does calculate_converted_score provide?** It gives three key pieces of intelligence: the total score out of 100, a qualification status (Cold/Warm/Hot/MQL/SQL), and an estimated range for conversion probability. **Do I need to manually provide all lead data?** No. The MCP handles the heavy lifting by first calling query_lead_profile_data, which retrieves foundational attributes automatically from your connected sources before scoring anything.