MCP 서버 구현은 연구 프로젝트, 참가자, 인터뷰, 관측, 코드, 테마 및 발견의 구조화 표현을 가능하게하는 품질 연구 지식 그래프를 관리하기위한 도구를 제공합니다. 이 서버는 qualitative 연구원이 데이터를 구성하고 분석 프로세스를 추적하고, 테마를 개발하고 풍부한 텍스트 데이터의 통찰력을 생성합니다.

특징

• **Persistent Research Context **: 여러 분석 세션에서 연구 entities 및 관계의 구조적 지식 그래프 유지
• Study Session Management: 독특한 ID와 기록 진행 시간을 가진 연구 분석 세션을 추적
• Thematic Analysis: 데이터 소스를 통한 기관 및 트랙 출현
• ** 코딩 프레임 워크 **: hierarchical 코딩 구조 및 트랙 코드 응용 프로그램 관리
• **Participant 관리 **: participant 데이터, 인구 통계 및 기여
• ** 데이터 소스 조직**: 인터뷰, 관찰 및 문서 구성 -Research 문제 추적: 특정 연구 질문에 대한 링크 데이터
• Memo Writing: 연구 과정에서 문서 분석 통찰력

Qualitative Researcher MCP Server

An MCP server implementation that provides tools for managing qualitative research knowledge graphs, enabling structured representation of research projects, participants, interviews, observations, codes, themes, and findings. This server helps qualitative researchers organize their data, track their analysis process, develop themes, and generate insights from rich textual data.

Features

• Persistent Research Context: Maintain a structured knowledge graph of research entities and relationships across multiple analysis sessions
• Study Session Management: Track research analysis sessions with unique IDs and record progress over time
• Thematic Analysis: Organize and track emergent themes across data sources
• Coding Framework: Manage hierarchical coding structures and track code applications
• Participant Management: Track participant data, demographics, and contributions
• Data Source Organization: Organize interviews, observations, and documents
• Research Question Tracking: Link data to specific research questions
• Memo Writing: Document analytical insights throughout the research process
• Chronological Data Analysis: Explore data in temporal sequence
• Code Co-occurrence Analysis: Identify relationships between different codes
• Methodology Documentation: Track methodological decisions and approaches

Entities

The Qualitative Researcher MCP Server recognizes the following entity types:

• project: Overall research study
• participant: Research subjects
• interview: Formal conversation with participants
• observation: Field notes from observational research
• document: External materials being analyzed
• code: Labels applied to data segments
• codeGroup: Categories or families of related codes
• memo: Researcher’s analytical notes
• theme: Emergent patterns across data
• quote: Notable excerpts from data sources
• literature: Academic sources
• researchQuestion: Formal questions guiding the study
• finding: Results or conclusions
• status: Entity status values (active, completed, pending, abandoned)
• priority: Priority level values (high, low)

Relationships

Entities can be connected through the following relationship types:

• participated_in: Links participants to interviews/observations
• codes: Shows which codes apply to which data
• contains: Hierarchical relationship (e.g., codegroup contains codes)
• supports: Data supporting a theme or finding
• contradicts: Data contradicting a theme or finding
• answers: Data addressing a research question
• cites: References to literature
• followed_by: Temporal sequence
• related_to: General connection
• reflects_on: Memo reflecting on data/code/theme
• compares: Comparative relationship
• conducted_by: Person who conducted data collection
• transcribed_by: Person who transcribed data