partner-polaris
Analyzes AWS Partner Polaris levels, training depth, and certification status for partners across countries, enabling L3 gap analysis and benchmarking.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@partner-polarisShow Korea partner Polaris status"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Partner Polaris MCP Server
AWS Partner Polaris Level 분석 MCP 서버. T&C 팀원들이 AI 어시스턴트를 통해 파트너 교육 깊이(Training Depth)를 조회, 계산, 벤치마크할 수 있습니다.
Tools
Tool | Description |
| 국가별 파트너 Polaris 레벨 현황 조회 |
| 입력값 기반 레벨 계산 (What-if 시뮬레이션) |
| L3 달성 갭 분석 + 우선순위 액션 가이드 |
| 국가간 Polaris 벤치마크 비교 |
| 공식 Polaris 프레임워크 기준 조회 |
Related MCP server: DataClaw MCP Server
설치
1. Clone
git clone https://github.com/twkim1122/polaris_mcp.git
cd polaris_mcp2. Install
pip install -e .Python 3.10+ 필요. 가상환경 사용 권장:
python -m venv .venv .venv\Scripts\activate # Windows source .venv/bin/activate # macOS/Linux pip install -e .
3. 데이터 추가
data/ 폴더에 자국 파트너 데이터 CSV를 추가합니다:
cp data/sample_korea.csv data/japan.csv # 템플릿 복사 후 수정커스텀 경로를 사용하려면 환경변수를 설정합니다:
export POLARIS_DATA_DIR=/path/to/your/data # macOS/Linux
set POLARIS_DATA_DIR=C:\path\to\your\data # WindowsMCP 클라이언트 설정
Kiro
~/.kiro/settings/mcp.json:
{
"mcpServers": {
"partner-polaris": {
"command": "python",
"args": ["-m", "partner_polaris_mcp.server"],
"cwd": "C:\\Users\\<your-alias>\\partner-polaris-mcp\\src",
"env": {
"POLARIS_DATA_DIR": "C:\\Users\\<your-alias>\\partner-polaris-mcp\\data"
}
}
}
}Claude Desktop
claude_desktop_config.json:
{
"mcpServers": {
"partner-polaris": {
"command": "python",
"args": ["-m", "partner_polaris_mcp.server"],
"cwd": "C:\\Users\\<your-alias>\\partner-polaris-mcp\\src",
"env": {
"POLARIS_DATA_DIR": "C:\\Users\\<your-alias>\\partner-polaris-mcp\\data"
}
}
}
}Amazon Q Developer (VS Code)
.vscode/mcp.json:
{
"servers": {
"partner-polaris": {
"command": "python",
"args": ["-m", "partner_polaris_mcp.server"],
"cwd": "${workspaceFolder}/src",
"env": {
"POLARIS_DATA_DIR": "${workspaceFolder}/data"
}
}
}
}데이터 포맷
CSV 컬럼
partner_name,tier,country,total_certs_ttm,foundational_certs,associate_certs,professional_certs,specialty_certs,ilt_sessions_total,ilt_sessions_int_plus,sb_subscription_engagements,sb_hands_on_completions,active_certs_3y데이터 소스
QuickSight "Global Partner Training & Certification" 대시보드
Self Service - Partner Certifications 시트
Candidate Country 필터 → 자국 선택 → Export
예시 질문
"Korea 파트너 Polaris 현황 보여줘"
"Samsung SDS가 L3 달성하려면 뭐가 필요해?"
"Korea vs Japan 파트너 교육 비교"
"Premier 파트너가 250 certs, 200 Int+ 이면 레벨이?"
"Advanced 티어의 L3 요건이 뭐야?"Polaris 프레임워크 요약
Dual-Condition Rule
L2/L3 레벨 부여를 위해 두 조건 모두 충족 필요:
조건1: Total certifications >= 티어별 threshold
조건2: Engagement Points >= 티어별 threshold
Thresholds
Tier | L2 Certs | L2 Pts | L3 Certs | L3 Pts |
Select | 5 | 5 | 20 | 20 |
Advanced | 10 | 10 | 40 | 40 |
Premier | 100 | 100 | 400 | 400 |
Point 계산
L2 Points: ILT 1석 = 1pt, Cert 1개 = 1pt, SB Sub 5개 = 1pt
L3 Points: Int+ ILT 1석 = 1pt, Int+ Cert 1개 = 1pt, Hands-on SB 5개 = 1pt
Int+ = Associate + Professional + Specialty (Foundational 제외)
프로젝트 구조
partner-polaris-mcp/
├── src/partner_polaris_mcp/
│ ├── server.py # MCP Tool 정의 (FastMCP)
│ ├── polaris_engine.py # 레벨 계산 로직
│ ├── gap_analyzer.py # L3 갭 분석 & 추천
│ ├── benchmark.py # 국가간 벤치마킹
│ ├── data_loader.py # CSV 로딩 & 캐싱
│ └── models.py # 데이터 모델
├── data/
│ ├── polaris_criteria.json # 레벨 threshold 설정
│ ├── sample_korea.csv # 샘플 데이터
│ └── country_mapping.json # 리전/국가 매핑
├── pyproject.toml
└── README.mdContributing
data/에 자국 CSV 추가get_polaris_status(country="YourCountry")로 테스트PR 또는 Slack #tc-polaris-mcp로 피드백
License
Internal AWS use only.
This server cannot be deployed
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