MCP Fm Analyzer

🚀 UVL Analyzer MCP

The UVL Analyzer MCP is a Model Context Protocol (MCP) server designed for analyzing feature models written in the Universal Variability Language (UVL). It offers a set of tools to process and extract insights from feature models, such as identifying atomic sets and calculating the average branching factor.

🚀 Quick Start

The UVL Analyzer MCP is a Model Context Protocol (MCP) server tailored for analyzing feature models written in the Universal Variability Language (UVL). It equips users with a suite of tools to process and glean insights from feature models, including identifying atomic sets and computing the average branching factor.

✨ Features

This MCP supports the following operations:

1. Atomic Sets
   Identify atomic sets in the feature model. An atomic set is a group of features that always appear together in all configurations.

2. Average Branching Factor
   Calculate the average number of child features for each parent feature in the feature model, offering insights into the model's complexity.

3. Commonality
   Measure the frequency of a feature's occurrence in product line configurations, typically expressed as a percentage.

4. Configurations
   Generate all possible valid configurations of the feature model. Each configuration represents a valid product that can be derived from the model.

5. Number of Configurations
   Return the total number of valid configurations represented by the feature model.

6. Core Features
   Identify features that always appear in all valid configurations (mandatory features).

7. Count Leaf Nodes
   Count the number of leaf features in the feature model. Leaf features are those without child features.

8. Dead Features
   Identify features that cannot be included in any valid product configuration due to constraints and dependencies.

9. Estimated Number of Configurations
   Provide an estimate of the total number of different configurations that can be generated from the feature model.

10. False Optional Features
    Identify features that appear to be optional but must be included in every valid product configuration because they are affected by constraints.

11. Feature Ancestors
    Identify all ancestor features of a given feature in the feature model.

12. Filtering
    Filter and select a subset of configurations based on specified criteria.

13. Leaf Features
    Identify all leaf features in the feature model.

14. Maximum Depth
    Find the maximum depth of the feature tree, indicating the longest path from the root to a leaf node.

15. Satisfiability
    Check if a given model complies with the constraints defined in the feature model.

💻 Usage Examples

Basic Usage

To use this feature on Claude Desktop, add the following content to claude_desktop_config.json:

Docker

{
  "mcpServers": {
    "uvl_analyzer": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "mcp/uvlanalyzer"
      ]
    }
  }
}

NPX

{
  "mcpServers": {
    "uvl-analyzer": {
      "command": "npx",
      "args": [
        "-y",
        "@lbdudc/mcp-uvl-analyzer",
      ],
    }
  }
}

📦 Installation

Docker Build

docker build -t mcp/uvlanalyzer .

📄 License

This MCP server is released under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please refer to the LICENSE file in the project repository.