Volatility MCP Server

Name: Volatility MCP Server
Author: bornpresident

@bornpresident

代码仓库

关于 Volatility MCP Server

A Model Context Protocol (MCP) server that integrates Volatility 3 memory forensics framework with Claude

#volatility #memory-forensics #mcp-server

基本信息

分类

其他

许可证

MIT

运行时

python

传输方式

stdio

发布者

bornpresident

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "volatility": {
      "command": "python",
      "args": [
        "/path/to/volatility_mcp_server.py"
      ],
      "env": {
        "PYTHONPATH": "/path/to/volatility3"
      }
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Volatility MCP Server?

A Model Context Protocol (MCP) server that integrates the Volatility 3 memory forensics framework with Claude and other MCP-compatible LLMs. It enables forensic investigators to analyze memory dumps using natural language instead of complex commands, reducing technical barriers and accelerating analysis.

How to use Volatility MCP Server?

Clone the repository, install mcp and httpx packages, set the VOLATILITY_DIR variable in the script, and add the server to Claude Desktop’s configuration (claude_desktop_config.json). After restarting Claude, simply ask natural language questions about your memory dumps (e.g., “List all processes in the memory dump at path”).

Key features of Volatility MCP Server

Natural language memory forensics via LLMs
Process analysis and hierarchy visualization
Network connection examination in memory dumps
Malware detection (code injection, hidden processes)
DLL and module analysis per process
File object scanning across memory
Custom Volatility plugin execution
Automatic discovery of memory dump files in a directory

Use cases of Volatility MCP Server

Initial triage: quickly inspect process trees and network connections
Suspicious process investigation: examine command‑line arguments, DLLs, and open handles
Malware hunting: run malfind, detect hidden processes, and analyze parent‑child anomalies
Reducing digital forensics backlogs by automating analysis for Indian investigators