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hungtrungthinh/mcp-system-monitor

A Model Context Protocol (MCP) server implementation designed for AI agents to monitor and interact with Linux servers (Ubuntu, CentOS, RedHat, etc.). This server provides comprehensive system information including CPU, memory, disk, network, and process data through both MCP protocol and HTTP REST API

https://github.com/hungtrungthinh/mcp-system-monitor · commit f745d1b59c6e · scored 2026-11-13 · scoring prov-4+qual-2+sec-2

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Scores

Pillar Score Signal Confidence Coverage
AI provenance (estimated AI-generated share) 0 none-detected 41% 100%
Code quality 67 adequate 90% 100%
Security 82 adequate 85% 100%

AI provenance

No AI attribution or AI-consistent velocity signals detected.

Month Lines added Attributed to AI
2025-07 3,398 0%

Evidence

AI provenance describes how code was produced; it is not a quality judgment. Unless attribution is explicit, these are signals consistent with AI generation, not proof. Squash/rebase workflows, imported code, and rewritten history reduce accuracy.

Code quality

Code quality is adequate: test-to-source ratio 0.14, no CI test run detected; 7% of function code in complex or long functions; 5% duplicated lines. Weakest area: testing.

Area Score
Complexity (lizard) 82
Duplication 75
Code smells (Opengrep) 92
Tests and CI 37
Documentation 65
Type safety n/a
Process (Scorecard review/maintenance) n/a

Most complex functions:

Security

Security posture is adequate: no high-impact findings. Weakest area: posture.

Area Score
Secrets (gitleaks, tree + history) 100
Static analysis (Opengrep) 100
Vulnerable dependencies (OSV) n/a
CI workflows (zizmor) n/a
Posture (policy, updates, signing, lockfiles) 0
OpenSSF Scorecard n/a

Static signals only: the scorer never runs the project's code. Secret findings are pattern matches that were not verified, and secret values are never stored.

Repository profile

Language Lines
Rust 3,166
Shell 232

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