MCP Servers

Lumen MCP

Hybrid TypeScript/Java MCP server for analyzing JVM Flight Recorder files, enabling AI agents to diagnose exceptions, slow SQL, network latency, and performance bottlenecks.

README

🧠 SnipeFactory: Lumen MCP Engine

JVM Flight Recorder (JFR) Forensic Engine for LLM
Hybrid TypeScript/Java MCP Server for Advanced JVM Analysis

Lumen MCP is the analytical brain of the Lumen ecosystem. It leverages a high-performance Java engine to parse JFR files and a TypeScript bridge to provide a seamless Model Context Protocol (MCP) interface for AI agents like Gemini and Claude.

✨ Key Features

Hybrid Intelligence: Combines Java's deep JFR parsing power with Node.js's ecosystem compatibility.
Exception Forensics: Trace hidden exceptions (Swallowed Exceptions) with full call chains.
SQL & I/O Analytics: Pinpoint slow queries and socket latencies with line-level precision.
Smithery Ready: Fully compatible with Smithery CLI for instant distribution.

🚀 Installation & Setup

1. Build the Project

Lumen MCP requires both Node.js (18+) and Java (11+) to build.

npm install
npm run build

(This command runs tsc for the bridge and ./gradlew installDist for the engine.)

2. Register with Gemini CLI

You can now run Lumen using the Node.js bridge (Recommended):

{
  "mcpServers": {
    "lumen": {
      "command": "node",
      "args": [
        "/path/to/lumen-mcp/dist/server.js"
      ]
    }
  }
}

🔍 Diagnostic Tools

Tool	Description
`analyze_exceptions`	Analyzes exception throw points and call chains.
`analyze_jdbc_queries`	Identifies slow SQL queries and their source lines (Java 11+).
`analyze_network_io`	Detects socket latency (Essential for Java 8 triage).
`analyze_hot_methods`	Pinpoints code lines consuming the most CPU cycles.
`analyze_memory_usage`	Analyzes GC health and object allocation hotspots.
`analyze_lock_contention`	Identifies thread synchronization bottlenecks.

📖 Usage Example

"Gemini, use lumen to analyze data/incident.jfr. Tell me why the database response is slow."

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