What can you do with this server?

This server analyzes AWS Learning Needs Analysis (LNA) CSV data and automatically generates capability assessment reports, enabling AI agents to perform data analysis and report generation. Capabilities: * Analyze LNA Data (analyze_lna): Parse and analyze an LNA CSV file to return team-by-team competency status, including scores, certifications, and roles in JSON format. * Generate PPT Report (generate_report): Automatically create a full PowerPoint (.pptx) report from LNA CSV data, including radar charts, team profiles, executive summary, training recommendations, and next steps. Supports custom company name and team configuration. * Get Team/Overall Scores (get_team_scores): Retrieve course-by-course competency scores for a specific team or the entire organization in JSON format. * Get Recommendations (get_recommendations): Generate prioritized training course recommendations (urgent/high/medium priority) for a specific team or all teams based on LNA scores. * Get Individual Assessments (get_individual_assessments): Retrieve per-person competency evaluations including average score, top 3 strengths, top 3 weaknesses, and top 3 recommended training courses.

Which integrations are available for this server?

Analyzes LNA (Learning Needs Analysis) CSV data exported from Qualtrics to generate competency assessment reports in PPT format.

How do I use LNA-Report MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LNA-Report MCP Server Analyze the LNA CSV file and generate a PPT competency report." 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.

LNA-Report MCP Server

by twkim1122

Overview Schema Related Servers Score Discussions

Python

Local

🧠 LNA-Report MCP Server

AWS LNA(Learning Needs Analysis) CSV 데이터를 자동 분석하여 **역량강화 평가 분석 리포트(PPT)**를 생성하는 MCP(Model Context Protocol) 서버입니다.

MCP란? AI 에이전트가 외부 도구를 호출할 수 있게 해주는 표준 프로토콜입니다.
이 서버를 연결하면 AI가 LNA 데이터 분석과 리포트 생성을 자동으로 수행합니다.

✨ 주요 기능

Tool	기능	출력
`analyze_lna`	LNA CSV 전체 분석	JSON (팀별 점수, 자격증, 역할)
`generate_report`	PPT 리포트 자동 생성	PPTX (레이더 차트 포함)
`get_team_scores`	팀/전체 과정별 점수 조회	JSON (과정별 점수)
`get_recommendations`	교육 추천 과정 도출	JSON (긴급/높음/보통)
`get_individual_assessments`	개인별 역량 평가	JSON (강점/약점/추천교육)

Related MCP server: S3 Data Lake MCP Server

📋 요구사항

Python 3.10 이상
pip 패키지 관리자
Windows 10/11, macOS, 또는 Linux

🚀 설치 방법

Step 1: 리포지토리 Clone

git clone https://github.com/twkim1122/LNA_Report_MCP.git
cd LNA_Report_MCP

Step 2: 패키지 설치 (editable 모드)

pip install -e .

참고: 의존성이 자동 설치됩니다 (mcp[cli], pandas, python-pptx, matplotlib, numpy)

Step 3: 설치 확인

python -m lna_mcp.server --help

오류 없이 실행되면 설치 완료!

🔌 MCP 클라이언트 연동 설정

Option A: Amazon Quick 연동

Amazon Quick 열기
Settings → Capabilities → Connectors 이동
"+ Add MCP server" → Local (stdio) 선택
아래 값 입력:

항목	값
Name	`lna-report`
Command	`python`
Args	`-m`, `lna_mcp.server`
Working Directory	`<clone한 경로>/LNA_Report_MCP/src`

Save → 새 대화에서 "LNA 파일 분석해 줘" 입력하여 Tool 인식 확인

Option B: Kiro CLI 연동

~/.kiro/settings/mcp.json 파일에 추가 (없으면 새로 생성):

{
  "mcpServers": {
    "lna-report": {
      "command": "python",
      "args": ["-m", "lna_mcp.server"],
      "cwd": "<clone한 경로>/LNA_Report_MCP/src",
      "env": {}
    }
  }
}

Windows 예시:
"cwd": "C:\\Users\\<username>\\projects\\LNA_Report_MCP\\src"
macOS/Linux 예시:
"cwd": "/home/<username>/projects/LNA_Report_MCP/src"

Kiro CLI 재시작 후 Tool이 인식됩니다.

Option C: Claude Desktop 연동

claude_desktop_config.json에 추가:

{
  "mcpServers": {
    "lna-report": {
      "command": "python",
      "args": ["-m", "lna_mcp.server"],
      "cwd": "<clone한 경로>/LNA_Report_MCP/src"
    }
  }
}

📊 사용 방법

1. 전체 분석

AI에게 요청:

LNA CSV 파일을 분석해 주세요.
csv_path: "C:\경로\LNA_2026.csv"

2. PPT 리포트 생성

LNA 분석 리포트를 PPT로 만들어 주세요.
csv_path: "C:\경로\LNA_2026.csv"
output_path: "C:\출력\리포트.pptx"
company_name: "ACME Corp"

LNA 분석 리포트를 HTML로 만들어 주세요.

3. 특정 팀 교육 추천

클라우드 AX팀에 필요한 교육 과정을 추천해 주세요.

4. 개인별 평가

각 개인별 강점, 약점, 추천 교육을 알려 주세요.

🗂️ CSV 입력 형식

필수 사항

Qualtrics LNA Excel에서 'Raw Data' 시트를 CSV로 변환
⚠️ 'Student Heatmap' 시트가 아닌 'Raw Data' 시트 사용
Progress 컬럼이 반드시 포함되어야 함 (100% 완료 응답만 분석)
인코딩: UTF-8 BOM 권장

CSV 변환 방법

Qualtrics LNA Excel 열기
'Raw Data' 시트 선택
다른 이름으로 저장 → CSV UTF-8 (쉼표로 분리)(*.csv) 선택
저장

🏢 팀 설정 (team_config)

LNA CSV의 팀 컬럼은 Qualtrics 임베디드 변수(${e://Field/Team1} 등)로 되어 있어, 실제 팀명과 매핑이 필요합니다.

team_config JSON 형식

{
  "${e://Field/Team1}": {
    "name": "엔터프라이즈 구축/운영팀",
    "leader": "K",
    "mission": "클라우드 구축/운영"
  },
  "${e://Field/Team2}": {
    "name": "AX팀",
    "leader": "T",
    "mission": "클라우드 전환 컨설팅"
  }
}

팁: team_config 없이도 분석은 가능합니다. 이 경우 팀명이 원시값으로 표시됩니다.

📑 리포트 포함 내용 (PPT)

생성되는 PPT 리포트에는 다음이 포함됩니다:

표지 + 기밀 고지
LNA 추진 배경 및 목적
Executive Summary (강점/약점 영역)
Score 정의 기준 (1~5점 해석)
전사 역량 현황 + 레이더 차트
팀별 상세 분석 (프로필, 점수, 개별 레이더 차트)
팀별 비교 레이더 차트 (오버레이)
팀별 교육 추천 (긴급/높음/보통)
결론 및 Next Steps

📁 프로젝트 구조

LNA_Report_MCP/
├── pyproject.toml              # 패키지 설정 및 의존성
├── README.md                   # 본 문서
├── .gitignore
└── src/lna_mcp/
    ├── __init__.py
    ├── server.py               # MCP 서버 (Tool 정의)
    ├── analyzer.py             # LNA 분석 엔진
    └── report_generator.py     # PPT 리포트 생성기

🔄 업데이트 방법

서버가 업데이트되면:

cd LNA_Report_MCP
git pull
pip install -e .  # 의존성 변경 시

Amazon Quick / Kiro CLI 재시작 또는 MCP 서버 Reconnect

❓ FAQ

Q: Python 버전이 3.9인데 사용 가능한가요?

A: 3.10 이상이 필요합니다. python --version으로 확인 후, 필요시 업그레이드하세요.

Q: pip install -e . 에서 오류가 발생합니다

A: pip를 최신으로 업그레이드한 후 재시도:

pip install --upgrade pip
pip install -e .

Q: Tool이 인식되지 않습니다

A: cwd 경로가 src 폴더를 가리키는지 확인하세요. LNA_Report_MCP/src가 정확한 경로입니다.

Q: CSV 분석 시 "Progress 컬럼 없음" 오류

A: Qualtrics Excel에서 'Raw Data' 시트를 CSV로 변환했는지 확인하세요. 'Student Heatmap' 시트는 Progress 컬럼이 없습니다.

Q: 한글이 깨집니다

A: CSV 저장 시 UTF-8 BOM 인코딩을 사용하세요. Excel에서 CSV UTF-8 (쉼표로 분리) 형식으로 저장합니다.

Q: 레이더 차트가 생성되지 않습니다

A: matplotlib 한글 폰트 이슈일 수 있습니다. Windows는 'Malgun Gothic', macOS는 'AppleGothic'이 필요합니다. macOS 사용자는 src/lna_mcp/report_generator.py에서 폰트 설정을 변경하세요:

plt.rcParams["font.family"] = "AppleGothic"  # macOS

🏷️ Score 정의

Score	수준	의미
1	None	기본적인 관련 지식이 없는 상태
2	Basic	기본적인 이해력을 갖추고 있으나 업무 수행 불가
3	Functional	스킬 활용 업무 수행 가능, 필요시 AWS 지원 필요
4	Proficient	다양한 수준의 업무를 수행 및 적용 가능
5	Expert	전문가 역할 수행 및 컨설팅 수행 가능

📜 License

Internal use only — AWS T&C Korea

👤 Author

AWS T&C Korea Team
Contact: ktaewoo@amazon.com

Available Tools

5 tools

analyze_lnaA

LNA CSV 파일을 분석하여 팀별 역량 현황을 반환합니다.

Args: csv_path: LNA CSV 파일 경로 team_config: 팀 설정 JSON (선택). 예: {"Team1": {"name": "구축팀", "leader": "최지석", "mission": "클라우드 구축/운영"}}

ParametersJSON Schema

Name	Required	Description	Default
`csv_path`	Yes
`team_config`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It explains the purpose and parameters but does not disclose side effects (e.g., read-only or mutating), error handling, or output format details beyond the implied return of team capability status.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the core purpose. The Args section is cleanly structured with no superfluous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, the description adequately covers tool usage and parameter semantics. Minor gap: the expected CSV format is not described, but overall it is complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description effectively documents both parameters. It gives a clear definition for csv_path (LNA CSV file path) and team_config (optional JSON string with example), adding essential meaning beyond the schema's type and title.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes LNA CSV files to return team capability status. This distinguishes it from siblings like generate_report (creates a report) and get_individual_assessments (focuses on individuals).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites or exclusions, leaving the agent to infer context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_reportB

LNA CSV 파일을 분석하여 역량강화 평가 분석 리포트 PPT를 생성합니다.

Args: csv_path: LNA CSV 파일 경로 output_path: 생성할 PPT 파일 경로 team_config: 팀 설정 JSON. 예: {"Team1": {"name": "구축팀", "leader": "최지석", "mission": "클라우드 구축/운영"}} company_name: 고객사명

ParametersJSON Schema

Name	Required	Default
`csv_path`	Yes
`output_path`	Yes
`team_config`	No
`company_name`	No	고객사

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.3/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses the core behavior (analyze CSV, output PPT) but omits side effects, permissions, error handling, or constraints. With no annotations, a score of 3 reflects minimal but adequate behavioral disclosure for a report-generation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a single introductory sentence and a structured Args block. Every sentence adds value, though the Args section could be slightly more compact. Overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, return values need not be explained. However, the description lacks context about input CSV format, error scenarios, and report content scope. With 4 parameters and no annotations, some completeness gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must add meaning. The Args section provides clear descriptions for all 4 parameters, including an example for 'team_config' and default values for optional params. This compensates well for the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a PPT report from LNA CSV data. It uses a specific verb-resource pairing ('analyzes and generates') and implies a distinct purpose from sibling tools like 'analyze_lna' and 'get_individual_assessments', though not explicitly differentiating.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description only states what it does, not when it should be selected over siblings like 'analyze_lna' or 'get_recommendations'. No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_individual_assessmentsB

개인별 역량 평가 결과를 반환합니다 (이름, 팀, 평균점수, 강점3개, 약점3개, 추천교육3개).

Args: csv_path: LNA CSV 파일 경로 team_config: 팀 설정 JSON (선택)

ParametersJSON Schema

Name	Required	Description	Default
`csv_path`	Yes
`team_config`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.1/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description must convey behavioral traits. It only states what is returned, with no mention of side effects, authentication, file existence handling, or read-only nature. Minimal transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: a one-line purpose then a short Args list. It is front-loaded with the main action and result fields. No redundant sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 params, output schema present), the description covers the basic functionality and return fields. However, it lacks usage guidance and behavioral context, making it minimally complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. The Args section adds brief explanations ('LNA CSV file path', 'team setting JSON (optional)') but lacks detail on formats, defaults, or constraints. For two parameters, this is insufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns individual competency assessment results and lists the specific fields (name, team, average score, strengths, weaknesses, recommended trainings). The verb 'returns' and explicit resource make the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool vs. its siblings (analyze_lna, get_team_scores, etc.). The description does not indicate scenarios or alternatives, leaving the agent to guess.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_recommendationsB

팀별 교육 추천 과정을 반환합니다.

Args: csv_path: LNA CSV 파일 경로 team_name: 팀명 (비어있으면 전체)

ParametersJSON Schema

Name	Required	Description	Default
`csv_path`	Yes
`team_name`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full burden. It discloses the tool returns recommendations based on a CSV file and optional team name, but does not mention side effects, permissions, or error conditions. It is adequate for a simple read operation but lacks depth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: three lines with a clear front-loaded Korean sentence. Every phrase is necessary with no wasted words. Efficient communication.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists (not shown), the description need not detail return values. However, it does not explain the nature of recommendations, how the CSV is used, or any dependencies. Minimal but sufficient for a simple tool with few parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description adds meaning. It explains csv_path as 'LNA CSV file path' and team_name as 'team name (empty for all)'. This adds context beyond the schema titles, though examples or format constraints would improve it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns team-specific training recommendations (verb+resource). It distinguishes from siblings like get_individual_assessments and get_team_scores by focusing on recommendations. However, it could be more specific about the source (LNA CSV) and the context of 'education recommendations'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives (e.g., analyze_lna, generate_report). The description does not mention scenarios, prerequisites, or when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_team_scoresA

특정 팀 또는 전체의 과정별 역량 점수를 반환합니다.

Args: csv_path: LNA CSV 파일 경로 team_name: 팀명 (비어있으면 전체)

ParametersJSON Schema

Name	Required	Description	Default
`csv_path`	Yes
`team_name`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.5/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavior. It only states that scores are returned, but omits details such as error handling for invalid csv_path, performance implications, or what 'competency scores' entail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise—two sentences plus parameter documentation—with the purpose stated upfront. No unnecessary words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has an output schema, so the description does not need to detail returns. However, it lacks context on behavioral aspects (e.g., data format, error behavior) and does not explain how team_name interacts with the CSV. Adequate but with gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds meaningful context: csv_path is the 'LNA CSV file path' and team_name defaults to all teams when empty. This clarifies the parameters beyond the schema's minimal titles.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns competency scores by process for a specific team or all teams. It uses a specific verb ('returns') and resource ('competency scores'), and distinguishes from siblings that focus on individual assessments or analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like get_individual_assessments or get_recommendations. It merely states what it does without indicating context or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesv1.0.0Jun 19, 2026
- First observedanalyze_lna
- First observedgenerate_report
- First observedget_individual_assessments
- First observedget_recommendations
- First observedget_team_scores

Install Server

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TDQS

A3.7/5.0

Disambiguation4/5

Tools have distinct purposes: analysis, report generation, individual assessments, recommendations, and team scores. Minor potential overlap between 'analyze_lna' (team capability status) and 'get_team_scores' (course-specific scores) but descriptions clarify differences.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern (analyze_lna, generate_report, get_individual_assessments, get_recommendations, get_team_scores). No deviations.

Tool Count5/5

Server has 5 tools, well-scoped for its purpose of analyzing LNA data and generating reports and recommendations. Not too few or too many.

Completeness4/5

Covers main operations: analysis, report generation, individual assessments, recommendations, and team scores. Missing a tool to manage team configurations or validate inputs, but not essential for core functionality.

Maintenance

ActivityMaintained

ResponsivenessSyncing

Resources

GitHub Repository

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MCP directory API

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curl -X GET 'https://glama.ai/api/mcp/v1/servers/twkim1122/LNA_Report_MCP'

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🧠 LNA-Report MCP Server

✨ 주요 기능

📋 요구사항

🚀 설치 방법

Step 1: 리포지토리 Clone

Step 2: 패키지 설치 (editable 모드)

Step 3: 설치 확인

🔌 MCP 클라이언트 연동 설정

Option A: Amazon Quick 연동

Option B: Kiro CLI 연동

Option C: Claude Desktop 연동

📊 사용 방법

1. 전체 분석

2. PPT 리포트 생성

3. 특정 팀 교육 추천

4. 개인별 평가

🗂️ CSV 입력 형식

필수 사항

CSV 변환 방법

🏢 팀 설정 (team_config)

team_config JSON 형식

📑 리포트 포함 내용 (PPT)

📁 프로젝트 구조

🔄 업데이트 방법

❓ FAQ

Q: Python 버전이 3.9인데 사용 가능한가요?

Q: pip install -e . 에서 오류가 발생합니다

Q: Tool이 인식되지 않습니다

Q: CSV 분석 시 "Progress 컬럼 없음" 오류

Q: 한글이 깨집니다

Q: 레이더 차트가 생성되지 않습니다

🏷️ Score 정의

📜 License

👤 Author

Available Tools

Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

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