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by lbdudc • Modeling & Simulation
An MCP server that analyzes Universal Variability Language (UVL) feature models to extract insights such as configurations, atomic sets, dead features, and metrics.
Analyze UVL feature models to enumerate valid product configurations and estimate configuration counts.
Detect modeling issues such as dead features, false optional features, and core features to improve model quality.
Compute metrics (average branching factor, max depth, leaf counts) and extract structural information (atomic sets, ancestors) for variability management.
The UVL Analyzer MCP processes feature models written in UVL and provides tools to identify atomic sets, core and dead features, leaf counts, branching factors, and to generate or estimate valid configurations. It includes checks for satisfiability, feature ancestry, false optional features, and filtering of configurations. The server is packaged for use via Docker or NPX and is distributed under the MIT License, making it easy to integrate into tooling like Claude Desktop for automated variability analysis.
This operation identifies atomic sets in a feature model.An atomic set is a group of features that always appear together across all configurations of the model. These sets help in simplifying and reducing the complexity of the model by grouping features that behave as a single unit.
This calculates the average number of child features per parent feature in the feature model. It provides insight into the complexity of the model.
Measures how often a feature appears in the configurations of a product line, usually expressed as a percentage. Features with high commonality are core features.
Generates all possible valid configurations of a feature model. Each configuration represents a valid product that can be derived from the feature model.
Returns the total number of valid configurations represented by the feature model.
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