# Feature Flag Rollout Calculator MCP for AI Agents MCP > Feature Flag Rollout Calculator helps you manage complex feature rollouts with precision. It handles deterministic user assignment, predicts how many users will see a new feature at each stage, and calculates the sample sizes needed for valid A/B testing. It removes the guesswork from your deployment cycle, ensuring that your users have a consistent experience while you scale features safely across your production environment. ## Overview - **Category:** developer-tools - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_lSCWqvwFeIxZT5aRq7pxIFPfRB4jVj5nQX8h1rfL/mcp - **Tags:** feature-flags, rollout, statistics, devops, experimentation ## Description The Feature Flag Rollout Calculator helps you manage complex feature rollouts with precision. When you're pushing new code to production, you need to know exactly who's seeing what without creating a mess of inconsistent states. This MCP takes the math off your plate, letting your agent handle the heavy lifting of rollout logic. You check if a user is part of a specific rollout using consistent hashing, which means they won't flip-flop between versions every time they refresh the page. It also helps you plan the trajectory of a launch by predicting exactly how many people will be impacted at 10%, 25%, or 50% rollout. This level of predictability is huge for coordinating with support teams and setting expectations for stakeholder updates. If you're running experiments, it ensures your data is actually meaningful by calculating the right sample sizes to spot regressions. You stop worrying about your results being skewed by a small group of users or being too noisy to act on. It even helps you spot where different feature flags might overlap so you don't accidentally break things for a specific group of users who happen to be in two different test buckets at once. It's a core part of the Vinkius ecosystem designed to make your deployment cycle more predictable and less prone to human error. By offloading these calculations to your agent, you can focus on writing code and shipping features rather than wrestling with probability math and manual user tracking. ## Tools ### analyze_cohort_overlap Estimates where two different feature flag populations intersect to prevent unintended behavior. ### calculate_statistical_thresholds Determines the required user count in a treatment group to detect specific metric regressions. ### get_user_assignment Checks if a specific user falls within a rollout percentage using consistent hashing for stability. ### project_rollout_impact Predicts the exact number of users affected at every stage of a planned deployment schedule. ## Prompt Examples **Prompt:** ``` Check if user_8821 is in the 15% rollout for the new dashboard. ``` **Response:** ``` The user `user_8821` is currently included in the 15% rollout for the `new_dashboard` feature. ``` **Prompt:** ``` If we hit 40% rollout, how many users are we looking at? ``` **Response:** ``` Based on your current active user base, a 40% rollout will affect approximately **12,400 users**. ``` **Prompt:** ``` What's the overlap between the 'dark_mode' flag at 50% and the 'beta_search' flag at 30%? ``` **Response:** ``` The potential overlap between these two flags ranges from a **minimum of 0% to a maximum of 30%**. ``` ## Capabilities ### Verify user eligibility Check if a specific user falls within a rollout percentage using consistent hashing for stability. ### Predict rollout volume Get the exact number of users affected at every stage of a planned deployment schedule. ### Calculate experiment sizes Determine the required user count in a treatment group to detect specific metric regressions. ### Identify cohort overlap Estimate where two different feature flag populations intersect to prevent unintended behavior. ### Ensure deterministic assignment Maintain a consistent user experience by ensuring users don't flip-flop between feature versions. ## Use Cases ### Verifying user inclusion in a rollout An engineer asks their agent if a specific ID is part of a new UI rollout. The agent uses the tool to confirm, preventing the dev from having to manually check the hashing logic. ### Predicting impact for a major launch A product manager wants to know how many users will see a new checkout flow. The agent calculates the exact count based on the current user base and the target rollout percentage. ### Detecting flag collisions A data scientist is worried about two flags colliding. They ask the agent to check the overlap between a new search flag and an old search flag to see if they'll interfere. ### Planning a multi-stage deployment A DevOps lead wants to see the impact of a multi-stage rollout. They ask the agent to project the user count for 5%, 20%, and 50% stages to prepare the support team. ## Benefits - Stop guessing which users see which features by using `get_user_assignment` to ensure every user gets a consistent experience. - Avoid breaking production by using `analyze_cohort_overlap` to see how multiple active flags might conflict with each other. - Get accurate deployment plans with `project_rollout_impact`, which tells you exactly how many users are affected at every percentage. - Ensure your A/B tests actually mean something by using `calculate_statistical_thresholds` to find the correct sample size. - Eliminate manual math errors by letting your agent handle the hashing and probability logic for all your feature flag configurations. ## How It Works The bottom line is you get the math right every time without doing the heavy lifting yourself. 1. Connect the Feature Flag Rollout Calculator to your AI client via Vinkius. 2. Ask your agent to check a user's status or project a rollout's impact. 3. Get back precise numbers for user counts, overlap percentages, or statistical thresholds. ## Frequently Asked Questions **How does the Feature Flag Rollout Calculator help with A/B testing?** It calculates the exact sample size you need to detect a regression. This ensures your results are statistically significant before you commit to a full launch. **Can I use the Feature Flag Rollout Calculator to check specific users?** Yes, you can ask your agent to verify if a specific user ID is included in a rollout. It uses consistent hashing to give you a definitive yes or no. **How do I know how many people will see a new feature?** You can ask the MCP to project the impact of different rollout stages. It will tell you the exact number of users affected at each percentage level. **Will the Feature Flag Rollout Calculator help prevent bugs from overlapping flags?** Yes, it estimates the intersection of multiple concurrent flags. This helps you identify if two features might be clashing for the same group of users. **Does the Feature Flag Rollout Calculator handle the actual toggling of flags?** No, this MCP handles the math and logic for rollouts. You still need your own system to store and toggle the flags themselves. **Why should I use this instead of a spreadsheet?** It's much faster and more accurate. It uses the same hashing logic your production system uses, so the numbers you see in your agent's chat are exactly what your users will experience. **How does the user assignment work?** 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. **Can I use this for A/B testing?** Yes. You can use `calculate_statistical_thresholds` to determine the necessary sample size to detect a specific Minimum Detectable Effect (MDE) with your desired confidence level. **How do I estimate the impact of a multi-stage rollout?** Use `project_rollout_impact` by providing your total population size and a schedule of percentages to see both active and incremental user counts at each stage.