runtime-mcp-server

runtime-mcp-server

Provides AI coding agents with real-time visibility into local development runtime state, enabling them to tail application logs, inspect ports, monitor process metrics, and diagnose network errors.

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README

Runtime Diagnostic MCP Server (runtime-mcp-server)

A local Model Context Protocol (MCP) server that provides AI coding agents (like OpenAI Codex CLI) with real-time runtime diagnostics, process monitoring, and log analysis for local development environments.

🎯 Purpose

AI coding agents are great at inspecting static code, but blind to runtime execution state. This server bridges that gap by giving AI agents programmatic, read-only tools to observe running processes, analyze crash logs, inspect active ports, and monitor system resources.

🛠️ Core Tools (v1 Features)

  1. tail_app_logs

    • Description: Reads the most recent lines from an explicitly provided local application log file.
    • Goal: Hands recent stack traces and error output to Codex without manual copy-pasting.
  2. inspect_ports

    • Description: Scans active local network ports (e.g., 3000, 8080) and identifies occupying process IDs (PIDs).
    • Goal: Automatically resolves EADDRINUSE / "Port already in use" errors and can safely signal zombie processes.
  3. get_process_metrics

    • Description: Reports per-core CPU and memory usage for an already-running local process. A process using one fully utilized core reports approximately 100%; multi-threaded processes may exceed 100%.
    • Goal: Helps Codex detect infinite loops, memory leaks, and runaway background scripts in real time.
  4. diagnose_runtime

    • Description: Combines port, process, and log evidence into a single deterministic runtime diagnosis.
    • Goal: Gives Codex a concise health report and evidence-backed next steps for a local application.
  5. capture_network_errors

    • Description: Inspects local failed HTTP/gRPC responses, CORS headers, and status codes.
    • Goal: Pinpoints root causes for broken frontend-to-backend local API connections.

🏗️ Technical Stack

  • Language: TypeScript / Node.js
  • Protocol: Model Context Protocol (MCP) via @modelcontextprotocol/sdk
  • Transport: Standard Input/Output (stdio)
  • System APIs: child_process, fs/promises, psutil / system process utilities

🚀 Getting Started

Install dependencies and compile the TypeScript source:

npm install
npm run build

Run the automated process-metrics tests:

npm test

Start the MCP server:

npm start

The server connects over stdio and currently provides port inspection, explicit log-file reading, live process metrics, and a combined runtime diagnosis tool.

🚀 Codex CLI Integration

Configured in ~/.codex/config.json (or Codex client settings):

{
  "mcpServers": {
    "runtime-diagnostics": {
      "command": "node",
      "args": ["/Users/USERNAME/Documents/Codex/runtime-mcp-server/build/index.js"]
    }
  }
}

📋 Development Roadmap

  • [x] Initialize TypeScript & @modelcontextprotocol/sdk project
  • [x] Implement inspect_ports tool
  • [x] Implement tail_app_logs tool
  • [x] Implement get_process_metrics tool
  • [x] Implement diagnose_runtime tool
  • [ ] Add integration tests with Codex CLI

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