MCP Workflow Guide for UX Researchers

Why Researchers Love MCP

UX researchers already know how to do research. The bottleneck is not skill — it is operational overhead. Scheduling, transcribing, coding, synthesizing, reporting. Koji MCP eliminates the overhead so you can focus on what matters: understanding users.

Here is what changes with MCP:

• Study setup: Minutes instead of hours (Claude generates methodology-aligned briefs)
• Transcript review: Ask Claude to summarize and highlight key moments
• Cross-study analysis: Compare themes across projects in one conversation
• Reporting: Generate and publish stakeholder reports conversationally
• Data export: Structured JSON for your insight repository

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Multi-Study Management

Researchers typically run 3-5 studies simultaneously. MCP makes this manageable:

"List all my active studies with their interview counts"

Get a quick dashboard of everything in flight. Then drill into any study:

"Show me the latest interviews from my onboarding study — any negative sentiment?"

Comparing Across Studies

This is where MCP shines for researchers. You can ask Claude to synthesize across your entire research portfolio:

"Get the study data from my onboarding study and my retention study. Are there overlapping themes? What shows up in both?"

Claude pulls structured data from both studies and identifies patterns that cross project boundaries — something that is incredibly time-consuming to do manually.

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Transcript Analysis Workflow

Rapid Screening

Instead of reading every transcript start to finish:

"Show me interviews from my study that have themes related to 'pricing' or 'value'"

Claude filters by cached theme data and surfaces the relevant interviews.

Deep-Dive Reading

When you find an interview worth studying:

"Show me the full transcript for this interview. What are the key moments where the respondent expressed strong emotion?"

Claude reads the transcript and highlights pivotal moments — saving you from scanning hundreds of messages.

Coding and Theming

While Koji auto-generates themes, researchers often need to recode:

"Get the study data. What themes are you seeing that might not be captured in the automatic analysis? Look at the summaries and suggest additional themes."

Claude can suggest themes based on its reading of the summaries — a starting point for your codebook.

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Research Report Workflow

Generate from Evidence

"Generate a report from my usability study. I want it to include recommendations we can present to the design team."

The report includes citations linking every finding back to specific interviews, so stakeholders can verify the evidence.

Section-by-Section Review

"Show me just the executive summary and key takeaways from the report"

Review the high-level narrative first. Then:

"Now show me the theme analysis and question coverage"

Work through the report methodically without loading the entire document.

Publish for Stakeholders

"Publish the report so I can share it with the product team"

Get a public link that non-Koji users can access — no account required for viewing.

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Building a Research Repository

Export for Your Insight Hub

Researchers often maintain research repositories in Notion, Dovetail, or custom databases:

"Export the full data from my study — brief, respondents, transcripts, and report summary"

Get structured JSON that maps to your repository schema. Transcripts are paginated (max 10 per request) to handle large studies:

"Export transcripts 11-20 from my study"

Cross-Referencing Over Time

When a stakeholder asks "what do we know about onboarding?", you can pull insights from multiple studies:

"List all my completed studies. Which ones have themes related to 'onboarding' or 'first-time experience'?"

"Get the study data from those three studies and synthesize the key findings about onboarding"

This turns your research history into a searchable knowledge base through natural conversation.

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Research-Specific Tips

Methodology Matters for AI Interviewers

The methodology you choose directly shapes how the AI conducts interviews:

• Mom Test: The AI avoids hypotheticals and focuses on past behavior. It will not ask "would you use feature X?" — instead it asks about current workflows and pain points.
• JTBD: The AI explores the "switching moment" — what triggered the user to seek a solution and what alternatives they considered.
• Discovery: Broad exploration with minimal assumptions. Good for new problem spaces.

Choose your methodology based on your research question, and the AI interviewer follows the guardrails automatically.

Quality Over Quantity

Do not aim for 100 interviews just because you can. Qualitative research reaches saturation at 12-20 interviews for most topics. Monitor theme stability:

"Show me the top themes from my study. Have they changed since interview 15?"

If themes stop changing, you have likely reached saturation.

Voice vs. Text

Voice interviews tend to produce:

• Longer, more detailed responses
• More emotional expression
• More natural language patterns

Text interviews tend to produce:

• More precise, considered responses
• Easier to search and quote
• Lower respondent friction

Most researchers use voice for exploratory research and text for validation.

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Next Steps

• Tool Reference — Detailed parameter docs for all tools
• Best Practices — General MCP usage tips
• Thematic Analysis Guide — Master theme analysis
• How Many Interviews Are Enough? — Sample size guidance