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SCAST MCP Server

Provides static code analysis visuals by converting code into UML diagrams, AST trees, and Mermaid flowcharts via an MCP-enabled local server.

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Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
    "mcpServers": {
        "scast": {
            "command": "node",
            "args": [
                "/YOUR_INSTALL_DIR/SCAST/mcp/index.js",
                "/YOUR_WORKSPACE/",
                "C:\\\\Users\\\\DKZ\\\\OTHER_ALLOWED_DIR\\\\"
            ]
        }
    }
}

SCAST MCP Server enables you to analyze source code and generate visual representations such as UML diagrams, AST trees, and Mermaid flowcharts through a local Node-based service. You wire it into your MCP client to run static code analysis on a folder of code and retrieve structured visual outputs and keyword-based summaries for quick navigation.

How to use

You run the SCAST MCP Server as a local stdio service and connect your MCP client to it. Provide the path to the code you want analyzed, and SCAST will return visualizations and a keyword map that helps you understand the code structure and functionality.

How to install

Prerequisites you need before starting:

Node.js installed on your machine
npm package manager installed

Install and run the SCAST MCP Server locally using the following steps.

git clone https://github.com/davidkingzyb/SCAST.git
cd SCAST
npm install

Configure your MCP client to load the SCAST MCP Server. Use the following MCP configuration snippet to register the server as a stdio backend.

{
  "mcpServers": {
    "scast":{
      "command":"node",
      "args":[
        "/YOUR_INSTALL_DIR/SCAST/mcp/index.js",
        "/YOUR_WORKSPACE/",
        "C:\\Users\\DKZ\\OTHER_ALLOWED_DIR\\",
      ]
    }
  }
}

Available tools

scast_analysis

Analyzes a codebase to generate an AST, UML diagrams, and Mermaid flowcharts, returning a keyword list of classes, methods, and their purposes, plus a browser link to view the charts.

scast_retriever

Searches analyzed code by keywords derived from the AST, locating source definitions and returning relevant code snippets to support rapid Q&A using a RAG approach.