Mar 3, 2026 at 7:21 AM 6 signals analysed No manual reviews · fully automatedTrust Signal Breakdown low 23 sub-signals across 6 dimensions
Vulnerability & Safety ×0.25 0.0 CVEs, dependency health, and supply chain integrity
0 of 3 sub-signals with data
Known CVEs no data —
Weight redistributed to sub-signals with data
Dependency Health no data —
Weight redistributed to sub-signals with data
Supply Chain no data —
Weight redistributed to sub-signals with data
Operational Reliability ×0.15 4.0 Uptime, latency, error rates, and incident history
4 of 4 sub-signals with data
Uptime 35% 5.0
100.00% over 2 checks
via Health checks
Response Latency 25% 5.0
p99: 82ms, p50: 82ms
via Health checks
Error Rate 20% 1.0
100.00% error rate (2/2)
via Health checks
Incident History 20% 4.0
1 incidents in last 90 days
via Incidents table
Maintenance Activity ×0.15 0.0 Commit recency, release cadence, issue response, CI/CD
0 of 4 sub-signals with data
Commit Recency no data —
Weight redistributed to sub-signals with data
Release Cadence no data —
Weight redistributed to sub-signals with data
Issue Response no data —
Weight redistributed to sub-signals with data
CI/CD Presence no data —
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Adoption ×0.15 0.0 Downloads, stars, dependents, and growth trajectory
0 of 4 sub-signals with data
Download Volume no data —
Weight redistributed to sub-signals with data
GitHub Stars no data —
Weight redistributed to sub-signals with data
Dependent Packages no data —
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Growth Trend no data —
Weight redistributed to sub-signals with data
Transparency ×0.15 0.0 License, documentation, security policy, changelog
0 of 4 sub-signals with data
Open Source no data —
Weight redistributed to sub-signals with data
Documentation no data —
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Security Policy no data —
Weight redistributed to sub-signals with data
Changelog no data —
Weight redistributed to sub-signals with data
Publisher Trust ×0.15 1.3 Track record, org maturity, community standing
4 of 4 sub-signals with data
Track Record 30% 1.0
Internal: 1.0 (0 services), External: 1.0 (4 followers, 0 stars)
via Fabric index
Org Maturity 30% 2.0
User account, 0.7 years old
via GitHub
Community Standing 20% 1.0
0 public repositories
via GitHub
Cross-Platform 20% 1.0
Present on 1 platform(s): github
via Registry scan
Limited data available — 4 of 6 signals pending evaluation
About this scoreScored across 23 sub-signals in 6 dimensions Scoring engine v1 (beta) — actively being expanded Phase 1: Core sub-signal architecture (live) Phase 2: Permission scope & expanded collection (in progress)
Trust Assessment AI Assessment
한국투자 코딩도우미 MCP (KIS Code Assistant MCP)
한국투자 코딩도우미 MCP는 한국투자증권의 수많은 Open API 중에서 자연어 검색으로 관련 API를 찾고, 호출 예제(파라미터 포함)까지 자동 구성해주는 MCP 서버입니다.
"무엇을 하고 싶은지"만 말하면, 관련 API를 추천하고 예시 호출 코드를 만들어 드립니다.
- 자연어 검색: "삼성전자 현재가 보여줘", "해외주식 잔고 확인" 같은 자연어 문장으로 탐색
- 예제 호출 구성: 필요 파라미터와 응답 구조를 함께 안내
- 실시간 업데이트: 최신 API 정보를 항상 반영합니다
KIS Code Assistant MCP helps you find stock and trading APIs with simple natural language.
Just say what you want, and it builds example calls with parameters instantly.
Package Availability (30d)
100.00%
p50: 82ms · p99: 82ms
Avg Latency
81ms
averaged across 30d health checks
Weekly Downloads
—
no package registry data
Incidents & Alerts last 90 days
Score History 2 snapshots
Feb 24 Mar 2
Data Sources 6 indexed
◎
OSV.dev CVE database · vulnerability scanning for npm & PyPI packages
◈
GitHub API Commits, issues, releases, repo metadata, transparency checks
⬡
npm Registry Package metadata, weekly downloads, maintainers, dependencies
⬡
PyPI Package metadata, weekly downloads, dependency tree
△
HTTP Health Checks 15-min pings · uptime, latency, status monitoring
◎
PyPI Stats Download statistics and trends
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