MCP For Claude Marketplace
Accelerator Selection Scoring

Accelerator Selection Scoring MCP Connector for Claude

A+

Calculate weighted startup scores, adjust for interviewer bias, and evaluate cohort fit.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides a complete engine for ranking startup candidates in accelerator programs. It uses a weighted scoring model to calculate primary scores via calculate_application_score, applies corrective measures to mitigate interviewer tendencies with apply_bias_correction, and evaluates strategic alignment using evaluate_cohort_impact. Finally, it generates comprehensive selection analytics, including ranking percentiles and acceptance probabilities, through generate_selection_analytics.

startupacceleratorscoringselectionanalytics

4 tools expose this connector's capabilities to your AI agent.

calculate_application_score

Calculates the primary weighted score for a single application

evaluate_cohort_impact

Adjusts an application's score based on how well it fits the specific needs of the current cohort

generate_selection_analytics

Provides a holistic view of an application's standing, including percentile and acceptance likelihood

apply_bias_correction

Adjusts raw scores to mitigate the impact of individual interviewer tendencies

See how to talk to your AI agent using Accelerator Selection Scoring.

Calculate the score for a startup with Team: 8, Market: 9, Product: 7, Traction: 6, and weights: team=0.4, market=0.3, product=0.2, traction=0.1.

The total weighted score for the application is 7.7.

An interviewer gave a score of 7, but the cohort average is 8. What is the adjusted score?

The adjusted score has been moved toward the cohort average to mitigate bias.

What is the acceptance probability for a score of 8.5 when the historical threshold is 8.0 and the pool scores are [7.0, 7.5, 8.0, 8.5, 9.0]?

The application has a high acceptance probability and ranks in the 80th percentile.

The `calculate_application_score` tool uses a weighted model where you assign decimal weights to Team, Market, Product, and Traction scores. The sum of these weights must equal 1.0.

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