system-monitor
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@system-monitorshow me current CPU and memory usage"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🛡️ System Status & Config MCP Server (Agentic AIOps)
The ultimate "eyes and ears" for your AI assistants. Give LLMs (like Claude, Cursor, or your autonomous agents) safe, read-only, and token-optimized access to your server's physical reality.
🌟 Why This Exists?
Large Language Models (LLMs) possess vast knowledge of system administration, architecture, and debugging. However, they suffer from a "reality gap" — they know how to fix an OOM error, but they don't know your server's RAM usage, your active configuration paths, or your current logs.
If you give an AI raw shell access (bash), it is extremely dangerous (e.g., accidental rm -rf or crashing the server by catting a 50GB log file).
This MCP (Model Context Protocol) Server solves this by acting as a Secure, Read-Only Abstraction Layer. It empowers AI to perform sub-second, cross-platform infrastructure audits and root-cause analysis without risking system integrity.
🚀 Core Features
1. 📊 High-Concurrency System Probes (get_system_status)
Gathers real-time OS metrics across 17 distinct categories (CPU, Memory, Docker, Network, Processes, Windows WMI internals, etc.).
Smart Concurrency: Windows WMI/CIM queries are executed in parallel (
Promise.all), reducing response times from seconds to ~200ms.Cross-Platform: Normalizes complex metrics across Linux, macOS, and Windows into clean JSON.
2. 🔍 Intelligent Config Radar (discover_configs)
AI doesn't need to guess where your config files are.
Auto-locates 36+ well-known services (Nginx, Redis, SSH, WSL, VSCode, Git, etc.).
Directory Sniffing: Point it to a project directory (
directory: "/path/to/project"), and it will automatically detect hidden.files and configuration extensions (.yaml,.ini,.env).
3. 🧠 Token-Optimized Config Reader (read_config)
Reads and parses structured configurations (JSON, YAML, INI, TOML, etc.).
keys-onlyMode: Designed specifically for LLMs. If an AI needs to read a massivepackage-lock.json, this mode strips out heavy values and returns only the structural schema (e.g., replacing arrays with[Array of 8 items]), saving tens of thousands of tokens while preserving context.
4. 🛡️ Safe Log Tailing (read_log)
A purpose-built, high-performance log analyzer.
Reverse Chunk Reading: Directly reads 64KB chunks from the end of the file backwards. Never loads the entire file into memory (OOM-safe).
Native Grep: Search for keywords (e.g.,
grep: "ERROR") across massive log files.Circuit Breakers: Hard-capped at 1000 lines and a 10MB backwards-search limit to protect the Node.js event loop during high CPU loads.
🛠️ Installation & Setup
Prerequisites
Node.js >= 18.x
TypeScript installed globally or locally
Build from source
# Clone the repository
git clone https://github.com/yourusername/system-status-mcp.git
cd system-status-mcp
# Install dependencies
npm install
# Build the project
npm run buildUsage with Claude Desktop
Add the following configuration to your Claude Desktop claude_desktop_config.json:
Windows (%APPDATA%\Claude\claude_desktop_config.json):
{
"mcpServers": {
"system-monitor": {
"command": "node",
"args": ["D:/Project/MCP/status/dist/index.js"]
}
}
}macOS/Linux (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"system-monitor": {
"command": "node",
"args": ["/path/to/system-status-mcp/dist/index.js"]
}
}
}🧰 Available MCP Tools
Tool Name | Description | Key Arguments |
| Retrieve system metrics. |
|
| Scan for known configs or project dotfiles. |
|
| Parse a specific config file. |
|
| Safely tail and grep massive log files. |
|
🏗️ Architecture & Safety Highlights
Embedded Error Reporting: Instead of failing silently or returning
500 Internal Error, the server captures explicit filesystem errors (EACCES,ENOENT) and PowerShell timeouts, embedding them directly into the JSON response (e.g.,{ "_error": "Permission denied" }). This allows the AI to autonomously adjust its strategy (e.g., asking the user forsudoprivileges).Graceful Timeouts: All heavy subprocess executions (like Windows WMI) are strictly timed out at 15 seconds. If the CPU is pegged at 99%, the MCP server will gracefully report timeout failures rather than hanging the AI indefinitely.
Built for the Agentic Era. Open-sourced under the ISC License.
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