MCP For Claude Marketplace
Feature Flag Rollout Calculator

Feature Flag Rollout Calculator MCP Connector for Claude

A+

Calculate deterministic user assignment, rollout projections, and statistical sample sizes for feature flags.

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

This MCP server provides mathematical utilities for managing gradual feature rollouts. Use get_user_assignment to verify if a specific user falls within a rollout percentage using consistent hashing. Plan your deployment with project_rollout_impact to see how many users are added at each stage. Ensure experiment validity with calculate_statistical_thresholds to determine the required sample size for detecting regressions, and use analyze_cohort_overlap to estimate the intersection of multiple concurrent flags.

feature-flagsrolloutstatisticsdevopsexperimentation

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

analyze_cohort_overlap

Estimates the potential intersection of two different feature flag populations

calculate_statistical_thresholds

Determines the required number of users needed in a treatment group to detect a metric regression

get_user_assignment

Determines whether a specific user is included in a feature flag's current rollout percentage

project_rollout_impact

Predicts the number of users affected at each stage of a predefined deployment schedule

See how to talk to your AI agent using Feature Flag Rollout Calculator.

Is user 'user_123' enabled for feature 'new_ui' at a 25% rollout?

The user 'user_12<0xA0>3' is currently enabled for the 'new_ui' feature.

What is the minimum and maximum overlap if Flag A is at 30% and Flag B is at 45%?

The potential overlap between the two flags ranges from a minimum of 0% to a maximum of 30%.

How many users do I need for an experiment with a 5% baseline conversion, 1% MDE, and 95% confidence?

To detect a 1% absolute drop from a 5% baseline with 95% confidence, you require a treatment sample size of approximately 16,450 users.

The `get_user_assignment` tool uses consistent hashing of the User ID and Feature Key to ensure that a user's assignment remains deterministic at any given rollout percentage.

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