Qualitative Researcher MCP Server

A versatile tool for constructing and enhancing the qualitative research knowledge graph by adding new research elements, relationships, and observations.

When to use this tool:

• Creating new research entities (projects, participants, interviews, observations, codes, themes, memos, etc.)

• Establishing relationships between research elements (e.g., connecting participants to projects, codes to data segments)

• Adding observations, notes, or content to existing research entities

• Building the research corpus incrementally as data collection and analysis progress

• Organizing and structuring qualitative data within your research framework

• Documenting emerging themes, codes, and analytical insights during research

• Creating research questions and linking them to findings

• Building code hierarchies and thematic frameworks

• Setting status values for research activities and entities

• Assigning priorities to research tasks and activities

• Defining sequential relationships between research processes

Key features:

• Creates three distinct types of knowledge graph elements: entities, relations, and observations

• Supports specialized qualitative research entity types (projects, participants, interviews, observations, documents, codes, etc.)

• Validates entity and relation types against predefined standards for the qualitative research domain

• Handles batch creation of multiple entities or relations in a single operation

• Returns confirmation with details of created elements

• Ensures proper data typing and structure for the qualitative research knowledge graph

• Enables comprehensive documentation of the research process

• Supports status and priority assignment through entity-relation model

• Enables sequential relationships through precedes relation

Parameters explained:

1. type: The type of creation operation to perform

• Accepts: "entities", "relations", or "observations"

• Determines how the data parameter is interpreted

2. data: The content to add to the knowledge graph (structure varies by type):

• For "entities": An array of objects, each containing:

  • name: Unique identifier for the entity

  • entityType: One of the valid entity types (project, participant, interview, observation, document, code, codeGroup, memo, theme, quote, literature, researchQuestion, finding, status, priority)

  • observations: Array of strings containing notes or properties about the entity

• For "relations": An array of objects, each containing:

  • from: Name of the source entity

  • to: Name of the target entity

  • relationType: The type of relationship between entities (e.g., "participated_in", "codes", "has_status", "has_priority")

• For "observations": Either a single object or an array of objects, each containing:

  • entityName: Name of the entity to add observations to

  • contents: Array of strings with new observations to add

Valid 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

• priority: Entity priority values

Valid relation types:

• participated_in: Links participants to interviews/observations

• codes: Shows which codes apply to which data

• contains: Hierarchical relationship

• 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

• has_status: Links entity to its status

• has_priority: Links entity to its priority

• precedes: Entity comes before another entity in sequence

Status information:

• Valid status values include: planning, data_collection, analysis, writing, complete, scheduled, conducted, transcribed, coded, analyzed, emerging, developing, established, preliminary, draft, final, active, in_progress

• Status is assigned through the has_status relation type

Priority information:

• Valid priority values: high, low

• Priority is assigned through the has_priority relation type

Return information:

• JSON response indicating success or failure

• For successful operations:

  • Success flag set to true

  • Details of created elements in the "created" field (for entities/relations) or "added" field (for observations)

• For failed operations:

  • Success flag set to false

  • Error message describing the issue

Error handling:

• Validates entity types against the predefined list for qualitative research

• Validates relation types against acceptable standards

• Returns descriptive error messages for invalid inputs

• Gracefully handles type mismatches and formatting errors

You should:

• Use consistent naming conventions for entities to facilitate relationships and retrieval

• Begin by creating projects and participants before more specific research elements

• Add detailed observations to entities to enhance context and retrievability

• Create relationships to build a comprehensive network of interconnected research data

• Use has_status relations to track the progress of research activities

• Use has_priority relations to indicate important research elements

• Use the precedes relation to establish sequences in research processes

• Use observations to document the evolution of codes, themes, and analytical thinking

• Regularly update entity observations as your understanding evolves

• Build hierarchical structures using relations (e.g., codes within code groups, themes connecting multiple codes)

• Document the full research journey by adding memos tied to specific analytical moments

• Link quotes to codes, themes, and findings to maintain evidential chains