MetroAI — KOLAS Compliance OS

Measurement uncertainty MCP server + 6 AI agents + verifiable audit trail. ISO/IEC 17025 + KOLAS-accredited laboratories.

CI License: MIT Python Version Tests MCPize Glama Maintenance Streamlit Awesome MCP PR

---

What is this?

A compliance operating system for Korea's 1,200+ KOLAS-accredited testing, calibration, RMP, and inspection institutions — and a measurement uncertainty MCP server that any MCP-compatible AI client (Claude Desktop, Cursor, VS Code, etc.) can call directly.

Built solo by Yongbeom Kim (KTR ISO 17034 cert. 2024-CM-007), who ran KOLAS audits at KIM's Reference with zero major non-conformities and got tired of redoing everything in Excel and email every quarter.

---

Quickstart — 30 seconds

As an MCP server (Claude Desktop, Cursor, etc.)

claude mcp add --transport http measurement-uncertainty \
  https://measurement-uncertainty.mcpize.run

→ then ask your AI: "Compute the GUM uncertainty for this voltage divider" or "Apply the TEM lattice template at 95% confidence".

As a web app (Streamlit)

pip install -e ".[dev,ml]"
streamlit run app.py

Live demo: metroai-gnbdv7pqq3quqsudb5pwvj.streamlit.app

As a Python library

pip install metroai

from metroai.templates import create_tem_lattice_calculator

calc = create_tem_lattice_calculator()
result = calc.calculate()
print(f"d = {result.measurand_value:.6f} nm "
      f"± {result.expanded_uncertainty:.4e} (k={result.coverage_factor:.2f})")

---

What MetroAI does

0. User-fit features (NEW in v0.7.0) — for KOLAS lab operators

After surveying the SEM-lab-operator journey end-to-end, four new features landed in v0.7.0 specifically to cover the applicant side of the accreditation workflow:

• 🔬 Domain-specific entry wizard — Landing page asks "Which instrument are you accrediting?" Five paths: SEM / TEM / AFM / OCD / general measurement. Each one routes to a domain dashboard with only the standards, KOLAS steps, uncertainty templates, and SOP checks that matter for that domain.
• 📚 Domain-specific KOLAS guides — Per-domain content: applicable ISO standards (4–6 per domain), six-step KOLAS accreditation process with typical pitfalls, 3–4 common nonconformities with root cause + MetroAI fix, and the typical uncertainty budget components. Content sourced from public ISO/SEMI/KOLAS-G-002 documents.
• 📝 KOLAS application form auto-generator — Fill an organization profile once → ReportLab generates a 7-section ISO/IEC 17025-style accreditation application PDF (organization info, scope, personnel, equipment, reference standards, environmental control, quality system). Generic template; final submission should be cross-checked against KAB's latest official form.
• 📋 Domain SOP rule-based checklist — Each domain ships with a 10-item SOP checklist derived from KOLAS-evaluator-perspective common findings. Real-time gap score and 1-click "add to orchestrator queue" for remediation.

End-to-end, the v0.7.0 changes raised our internal "lab-operator journey fit score" from 45% → 68% on a 7-stage scenario (entry → guide → form → KOLAS process → SOP check → simulation → end-to-end). The final 32% includes stages we can't automate ourselves (the "consulting + on-site evaluator hand-holding" piece of the journey).

1. Compliance OS — 6 AI agents (since v0.6.0)

Agent	Role	Data source
semi-intel	Semiconductor industry signals	DART (Korea FSS) + NTIS R&D feeds
job-scout	Personnel turnover signal	Public job postings (baseline stub)
kolas-monitor	KOLAS / KAB / KTR notice scan	knab.go.kr live fetch (with stub fallback)
kolas-audit-predictor	Next-audit risk prediction	Rule baseline + optional GBT model
orchestrator	Integrated P0/P1/P2 task queue	All other agents
schedule	Calibration / audit / review calendar	Internal events DB

Every agent output carries is_live / data_origin flags (live · stub · synthetic), so the UI can clearly distinguish authoritative data from heuristics.

2. Measurement uncertainty engine (since v0.5.0)

• GUM (ISO/IEC Guide 98-3) — symbolic partial derivatives, Welch–Satterthwaite, expanded U
• MCM (ISO/IEC Guide 98-3 Suppl. 1) — Monte Carlo with configurable n
• QMC — Sobol low-discrepancy sequence (verified ±0.003% agreement with GUM analytic on simple linear models)
• reverse_uncertainty — novel within prior-art search. Given a target combined U, compute the maximum allowed standard uncertainty per component. Not found in GUM Workbench, NIST Uncertainty Machine, or major open-source GUM tools as of 2026-05.

3. Nine calibration templates

Template	Domain	Standard
gauge_block	Length	KOLAS-G-002
mass	Mass (weights)	OIML R 111
temperature	Temperature (PRT)	ITS-90
pressure	Pressure	KOLAS-G-002
dc_voltage	DC voltage	KOLAS-G-002
tem_lattice (v0.6 new)	TEM d-spacing	Si CRM reference
sem_eds (v0.6 new)	SEM-EDS quantitative	ZAF, ISO 22489
afm_roughness (v0.6 new)	AFM surface roughness Sa/Sq	ISO 25178-2
ocd_scatterometry (v0.6 new)	OCD CD measurement	RCWA, SEMI MF-1789

4. Verifiable audit trail (NEW in v0.6.0)

• Ed25519 digital signatures (RFC 8032) — tamper-evident outputs
• W3C PROV-O provenance graphs (JSON-LD) — full input → model → output lineage
• Designed so a KOLAS auditor can verify no post-hoc tampering

5. Three MCP tools

Tool	Use case
calculate_uncertainty	GUM calculation across the 9 templates
pt_analysis	Proficiency Testing — z-score / En / zeta per ISO 13528 + 17043
reverse_uncertainty	Target-U → per-component limit allocation

---

Honest metrics — kolas-audit-predictor

5-fold CV on synthetic data: accuracy 60.6% ± 3.1pp · ROC-AUC 0.628 ± 0.038 · Brier 0.241 · F1 0.636 (n=2000 × 6 features, label noise 0.15)

• GradientBoostingClassifier (n_estimators=200, depth=3, lr=0.05)
• Top 3 feature importances: months_since_last_audit (0.34) · personnel_turnover (0.25) · sop_completeness (0.24) — aligned with domain intuition.
• External validation on real KOLAS audit outcomes is pending. Synthetic-data metrics do not imply real-world accuracy.
• A prior sandbox figure of 87.1% has been removed from all artifacts. See docs/HONESTY_NOTES.md for citation rules.

---

Standards compliance

• ISO/IEC 17025:2017 (testing & calibration laboratories)
• ISO/IEC Guide 98-3 (GUM) + Suppl. 1 (MCM)
• ISO 13528 + ISO 17043 (proficiency testing)
• ISO 18516 (microscope methods)
• ISO 25178-2 (areal surface texture)
• ISO 22489 (SEM-EDS quantitative)
• KOLAS-G-001 / G-002 (Korean accreditation guidelines)
• SEMI MF-1789 (OCD scatterometry)
• W3C PROV-O (audit provenance)
• RFC 8032 (Ed25519 signatures)

---

Streamlit app — v2-spec pages

v2 backbone (since v0.6.0):

1. 🏠 Landing (app.py) — KOLAS Compliance OS positioning + domain wizard
2. 🤖 6 Agents Dashboard (pages/11) — Quality Manager daily view, KPI strip + task queue
3. 📋 SOP Gap Analyzer (pages/12) — Technical Manager work surface, AI-detected gaps + v0.7 domain-specific checklist
4. 📰 KOLAS Feed (pages/13) — kolas-monitor regulatory news
5. 🎯 Audit Risk Detail (pages/14) — explainability, waterfall + AI reasoning + what-if
6. 📅 Ops Backbone (pages/15) — certificates / personnel / schedule

v0.7.0 P0 — lab-operator journey (NEW):

7. 🔬 SEM domain dashboard (pages/16) — SEM-EDS standards + KOLAS process + nonconformities + SOP checklist
8. ⚛️ TEM domain dashboard (pages/17) — lattice constant, ISO 29301 + Cs-corrector spec
9. 📐 AFM domain dashboard (pages/18) — surface roughness Sa/Sq/Sz per ISO 25178-2
10. 📏 OCD domain dashboard (pages/19) — Scatterometry / RCWA library matching per SEMI MF-1789
11. 📝 KOLAS application form (pages/20) — Fill-once → 7-section ISO 17025-style PDF (KAB-F-21 reference)

Plus the legacy v0.5 calibration / PT / certificate pages (pages/1–10).

---

Repository layout

metroai/
├── app.py                     ← v2-spec landing page (Streamlit entry)
├── app_v0_5_backup.py         ← Legacy v0.5 landing (backup)
├── pages/                     ← Streamlit multi-page
│   ├── 1_📐_불확도_계산.py     ← Uncertainty calculator (KR)
│   ├── 2_📊_PT_분석.py         ← PT analysis (KR)
│   ├── 3_📄_교정성적서.py      ← Calibration certificate PDF
│   ├── 4_🔄_불확도_역설계.py    ← Reverse uncertainty (novel)
│   ├── 11_🤖_6_Agents.py       ← v2 block 2: main dashboard
│   ├── 12_📋_SOP_갭_분석.py     ← v2 block 4: SOP gap analyzer
│   ├── 13_📰_KOLAS_피드.py      ← v2 block 5: regulatory feed
│   ├── 14_🎯_감사_위험_상세.py   ← v2 block 3: risk explainability
│   └── 15_📅_인증서_인력_일정.py ← v2 block 6: operations
├── metroai/
│   ├── core/                  ← GUM / MCM / model parsing
│   ├── agents/                ← 6 AI agents backbone
│   ├── audit/                 ← Ed25519 + PROV-O
│   ├── connectors/            ← KOLAS / DART / NTIS live fetch + stub fallback
│   ├── math/                  ← Sobol QMC
│   ├── ml/                    ← GBT audit-risk model + synthetic data
│   ├── templates/             ← 9 calibration templates
│   ├── schemas.py             ← Pydantic v2 input validation
│   ├── exceptions.py          ← MetroAIError hierarchy
│   └── mcp_server.py          ← MCP stdio server
├── tests/                     ← 80+ unit tests (pytest)
├── docs/
│   ├── HONESTY_NOTES.md       ← Citation rules
│   ├── v0.7.0_ROADMAP.md      ← Next 3 months
│   └── RELEASE_NOTES_v0.6.0.md
├── mcp_manifest.json          ← MCPize manifest (v0.6.0)
└── pyproject.toml

---

Roadmap (v0.7.0 — 2026-05 → 2026-08)

Reordered 5/19 around the lab-operator journey (after a virtual-user audit revealed v0.6.0 covered only 45% of the path-to-accreditation). Philosophy shifted from outbound-first → user-fit-first.

Priority	Item	Status	Goal
P0	Domain-specific entry wizard (SEM/TEM/AFM/OCD/general)	✅ shipped v0.7.0	Stage 1 of journey
P0	Domain-specific KOLAS guides	✅ shipped v0.7.0	Stage 2, 4
P0	KOLAS application form auto-generator	✅ shipped v0.7.0	Stage 3
P0	Domain SOP rule-based checklist	✅ shipped v0.7.0	Stage 5
P1	Real KOLAS audit data + GBT retrain	pending	Replace synthetic 60.6%
P2	HF Spaces migration	guide ready	Eliminate Streamlit Cloud sleep
P2	Cold email to 5 KOLAS labs (post-P0 fit ≥ 80%)	pending	First user signal
P2	Show HN + Reddit r/MachineLearning publish	drafts ready	External signal
P3	Consulting SOP guide (per-domain on-site eval prep)	needs author	Cover stage 7 partially
P3	LLM-assisted kolas-monitor (real inference)	stub now	Clear AI differentiation

See docs/v0.7.0_ROADMAP.md for the full plan.

---

Tech stack

• Python 3.10+ (tested on 3.10 / 3.11 / 3.12)
• Streamlit (web UI)
• sympy / numpy / scipy (numerical)
• Pydantic v2 (input validation)
• cryptography (Ed25519)
• scikit-learn (GBT model, optional [ml] extra)
• reportlab + openpyxl (PDF + Excel export)
• altair / plotly (visualizations)

---

Tests

pip install -e ".[dev,ml]"
pytest tests/ -v

Latest CI on Python 3.10 / 3.11 / 3.12 — 36 passing v0.6.0 tests plus the v0.5 legacy suite.

---

License

MIT License. See LICENSE.

---

Community

• GitHub Issues — bug reports, feature requests
• GitHub Discussions — Q&A, design discussion
• MCPize page — install + reviews: mcpize.com/mcp/measurement-uncertainty
• Glama listing — glama.ai/mcp/servers?query=metroai
• Email — [email protected] (KOLAS-side feedback especially welcome)

---

한국어 사용자를 위한 안내

본 프로젝트는 한국 KOLAS 인정 기관 실무자가 직접 사용할 수 있도록 한국어 페이지와 한국어 UI 를 지원합니다. 자세한 한국어 가이드는 docs/RELEASE_NOTES_v0.6.0.md 및 Streamlit 앱의 한국어 페이지들 (불확도 계산 / PT 분석 / 교정성적서 / 불확도 역설계 / KOLAS 로드맵 / 6 Agents 대시보드 / SOP 갭 분석 / KOLAS 피드 / 감사 위험 상세 / 운영 백본) 을 참고해주세요. cold-feedback 환영합니다 — [email protected].

---

Built with care by @kyb8801 · KIM's Reference KOLAS RMP cert. KRMPs-021 background.