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ihor-sokoliuk/mcp-searxng

https://github.com/ihor-sokoliuk/mcp-searxng.git · scanned 2026-05-16 01:59 UTC (4 weeks ago) · 10 languages

44 raw signals (7 security + 37 graph) 93rd percentile · Typescript · small (2-20K LoC) System graph score 71 (higher by 15)

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Complete repo analysis

Last scanned 4 weeks ago · v1 · 6 actionable findings from 1 signal source. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

JSON
Combined
81.9
/100
Security checks
93
6 findings
System graph
71
77.8% cov · 0 findings
Critical
0
click to filter
Agents
6
0 votes
Crowd
0
reported
Score breakdown â 2026-05-17-v4 calibration-aware
Component Sub-score Weight Contribution
structure_score 100.0 0.15 15.00
security_score 92.5 0.25 23.12
testing_score 85.0 0.20 17.00
documentation_score 75.0 0.15 11.25
practices_score 75.0 0.15 11.25
code_quality 80.0 0.10 8.00
Overall 1.00 85.6
Calibrated penalty buckets (security_score): web: 1.6 · authz: 2.0 · docker: 3.9
Severity distribution — click a segment to filter
Active filters: excluding tests × Reset all
Severity: Critical 0 High 2 Medium 3 Low 1 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 6 System graph 0 Crowd 0 Layer: Quality 1 Security 2 Cicd 3

Bug-class explainers. Each card groups findings of the same shape — these are the patterns most likely to ship to prod and reappear in future scans unless you systematically fix the cause, not just the instance.

No integrity bug-classes triggered on this scan. The 6 classes we check for — fragile runtime, duplicates, orphan pages, legacy markers, commented-out code, config drift — have zero matches. Solid foundation.
For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.
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