Qualitative Researcher MCP Server

The Qualitative Researcher MCP server is a specialized back‑end that transforms unstructured qualitative data into a richly interconnected knowledge graph. By exposing a set of tools and entities that mirror the workflow of qualitative scholars, it lets AI assistants like Claude keep a persistent research context across sessions, track analytic progress, and surface insights that would otherwise remain buried in notes or spreadsheets. For developers building AI‑augmented research assistants, this server removes the need to write custom data models for projects, participants, codes, and themes, enabling rapid integration of advanced analytic capabilities into conversational agents.

The server solves a core pain point for qualitative researchers: the fragmentation of data, coding schemes, and analytic memos across multiple tools. It offers a persistent research context that stores every entity—projects, participants, interviews, observations, documents, codes, code groups, memos, themes, quotes, literature references, research questions, findings—and the rich set of relationships that connect them. This unified graph lets an AI assistant recall past coding decisions, suggest related codes, or highlight contradictions in real time. By maintaining a study session state with unique IDs and timestamps, the server records where analysis stopped, what themes are emerging, and which tasks remain, making it trivial for an assistant to pick up a conversation where the researcher left off.

Key capabilities are articulated in plain language:

• Thematic and coding management – create hierarchical code groups, apply codes to data segments, and track co‑occurrence patterns.
• Participant and data source organization – link participants to interviews or observations, tag documents, and record collection dates.
• Memo and insight capture – write reflective notes that reference specific data or codes, automatically attaching them to the appropriate entities.
• Temporal and methodological tracking – order analysis steps with /, document methodological choices, and record triangulation across sources.
• Query‑friendly API – tools like provide a snapshot of current projects, recent data, and next actionable items, enabling conversational agents to surface contextually relevant information without manual searching.

Real‑world use cases include: a research assistant that suggests the next interview question based on unanswered research questions; an AI collaborator that flags contradictory quotes and proposes new themes; or a project dashboard where stakeholders can see progress, status updates, and priority areas directly from a chat interface. By integrating these capabilities into AI workflows, developers can build assistants that not only answer questions but actively guide the research process, ensuring rigor and transparency while freeing scholars to focus on interpretation.

What sets this MCP apart is its semantic richness and process awareness. The server’s relationship vocabulary—, , , , —mirrors the conceptual logic of qualitative analysis, allowing AI models to reason about data in a way that feels natural to human researchers. This depth of context, combined with the ability to persist across sessions, makes the Qualitative Researcher MCP a powerful foundation for any application that seeks to embed advanced qualitative analytics into conversational AI.