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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
AI provenance
0%
none-detected
- Confidence
- 41%
- Coverage
- 100%
Estimated share of AI-generated code. Explicit attribution is a lower bound.
Code quality
67/100
adequate
- Confidence
- 90%
- Coverage
- 100%
Complexity, duplication, lint density, tests, CI, docs and review process.
Security
82/100
adequate
- Confidence
- 85%
- Coverage
- 100%
Secrets, static analysis, vulnerable dependencies, CI and supply-chain posture.
- Repository: https://github.com/hungtrungthinh/mcp-system-monitor
- Commit:
f745d1b59c6e2bef976ace308324744cc8ae7aca - Generated: 2026-11-13T23:43:38+00:00
- Scorer 0.1.0, scoring prov-4+qual-2+sec-2, report schema v2
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.
-
Explicitly attributed share (lower bound): 0.0%
-
Unattributed share in AI-consistent velocity bursts: 0.0%
-
Estimated AI-generated share: 0.0%
-
Stylometry index: 10.43 per 1,000 lines (narrated steps 0, placeholders 4, assistant phrasing 0, emoji 46). Reported as a signal, not scored.
| Month | Lines added | Attributed to AI |
|---|---|---|
| 2025-07 | 3,398 | 0% |
Evidence
- placeholder or demo-only phrasing: //! of system monitoring data for demonstration purposes. (examples/dem)
- placeholder or demo-only phrasing: /// Simulate system metrics (replace with actual MCP server calls) (examples/dem)
- placeholder or demo-only phrasing: // In a real scenario, you would call the MCP server here (examples/dem)
- placeholder or demo-only phrasing: // For demo purposes, we'll simulate realistic data (examples/dem)
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 |
- Functions: 105; mean complexity 3.1, 90th percentile 7; 0 complex and 1 long functions
- Duplicated lines: 4.8% of 2,100 compared
- Tests: 427 test lines for 2,971 source lines (ratio 0.14); CI test commands: none detected; 100% of recent source commits also change tests
- Docs missing or thin: contributing, changelog
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 |
- Posture checks passed: none; missing: security policy, dependency updates, signed commits, lockfiles
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
- Source lines: 2,971; test lines: 427
| Language | Lines |
|---|---|
| Rust | 3,166 |
| Shell | 232 |