Alfred VoC Analysis MCP v0.1

Heum Alfred/DESK VoC facts를 CX intelligence로 변환하는 MCP 서버입니다. 구조는 명시적으로 Fact → Analysis → Synthesis로 분리됩니다.

Architecture

• fact.py: DB/API adapter가 구현할 FactRepository; Fact에는 AI 해석을 저장하지 않습니다.

• analysis.py: overview, issue intelligence, CX concern, emerging signal, trend driver, resolution effect를 deterministic Python으로 계산합니다.

• synthesis.py: 상세 Report, Executive Brief, 개선 Planning Signal DTO를 조합합니다.

• server.py: FastMCP tool adapter. 분석 코어는 MCP나 외부 LLM에 의존하지 않습니다.

입력 스키마는 FactRecord(extra="allow")로 source 확장 필드를 보존합니다. 실제 DB 스키마를 변경하지 않으며 production DB/API 연결은 FactRepository adapter로 추가합니다.

Related MCP server: NPS Chatbot MCP Server

MCP tools

• get_voc_overview

• analyze_issue_frequency, analyze_issue_severity, analyze_issue_persistence

• analyze_unresolved_issues, rerank_issue_priorities, compare_issue_portfolio

• analyze_customer_experience

• detect_emerging_signals

• analyze_voc_trends

• analyze_resolution_effect

• build_report_dataset, build_executive_brief, build_improvement_signals

Issue tools intentionally share one portfolio assessment so Frequency alone cannot contradict Priority Movement. Each issue result returns evidence, reason and confidence.

Run

python -m venv .venv
pip install -e ".[dev]"
pytest
alfred-voc-analysis-mcp

v0.1 ships an in-memory reference adapter. Embed the service and call configure_records, or implement FactRepository for voc-management DB/API.

Calibration

AnalysisConfig contains data-sufficiency and observation-window settings. Defaults are conservative technical defaults, not Heum policy. No opaque weighted score or fixed “N건=High” business rule is used.