Learn hub

Great design decisions are grounded in evidence. But finding the right evidence isn't always easy when it's spread across multiple teams, usability tests, interviews, and surveys.

‍

Instead of searching through reports or asking teammates if research already exists, Model Context Protocol (MCP) lets you ask questions about your research repository in natural language from AI tools like ChatGPT, Claude, Gemini, and Cursor.

‍

Whether you're designing a new feature, iterating on a prototype, or preparing for a design review, MCP helps you quickly bring user evidence into your workflow.

‍

Here are seven ways UX, product, and experience designers can use MCP throughout the design process.

‍

‍

1. Start every design project with what users already told you

‍

Before opening Figma, understand what users are trying to accomplish, where they're struggling, and what your team has already learned.

‍

Try asking:

• What have we already learned about onboarding?
• What usability issues have we identified in checkout?
• What are users trying to achieve when managing their account settings?
• What research should I review before redesigning navigation?

‍

Designer workflow

Before kicking off a redesign, ask MCP to summarize existing research. Use the findings to define design goals, identify constraints, and prioritize the problems worth solving before creating your first wireframe.

‍

‍

2. Validate design concepts before investing time in high-fidelity designs

‍

As ideas begin to take shape, use previous research to pressure-test your thinking. MCP can surface similar studies, recurring usability issues, and participant feedback that helps you refine concepts earlier.

‍

Try asking:

• Have we tested a similar design before?
• What patterns have users struggled with in previous prototypes?
• What should we avoid repeating?
• Which usability findings should influence this design?

‍

Designer workflow

While exploring concepts in Figma, keep an AI assistant open alongside your design files. Ask questions as you work so previous research continuously informs design decisions instead of becoming something you review once at the beginning.

‍

‍

3. Write stronger design rationale

‍

Design reviews often involve explaining why a particular solution was chosen. Use MCP to find supporting evidence from previous studies.

‍

Try asking:

• Find participant quotes supporting a simplified navigation.
• What evidence suggests users prefer this workflow?
• Which usability studies identified this problem?
• Show examples of participants struggling with this interaction.

‍

Designer workflow

Use participant quotes, findings, and usability observations directly in design specs, PRDs, or design review presentations to help stakeholders understand the reasoning behind your decisions.

‍

‍

4. Spot UX patterns across products and releases

‍

Looking across multiple studies can reveal broader experience patterns. MCP can identify recurring pain points, emerging behaviours, and themes that may influence future design priorities.

‍

Try asking:

• What usability issues appear across multiple product areas?
• Compare findings from our last five prototype tests.
• Which friction points have become more common over time?
• What navigation issues keep appearing across studies?

‍

Designer workflow

Before planning a larger redesign, review patterns across multiple past studies. These recurring themes often highlight systemic UX issues that individual projects miss.

‍

‍

5. Prepare for design critiques and stakeholder reviews

‍

Strong design presentations combine visual solutions with user evidence. Use MCP to generate summaries tailored to your audience.

‍

Try asking:

• Summarize the research supporting this redesign.
• What are the three biggest user pain points?
• Create an executive summary for stakeholders.
• What customer evidence supports prioritizing this work?

‍

Designer workflow

Generate concise summaries before design critiques, roadmap discussions, or leadership reviews, then pair them with your prototypes to show both the solution and the evidence behind it.

‍

‍

6. Plan better usability tests

‍

Designers frequently need to validate prototypes, but not every question requires a brand new study. MCP helps identify what has already been answered and where genuine knowledge gaps remain.

‍

Try asking:

• What questions about this flow are still unanswered?
• What assumptions should we validate?
• Which participant groups haven't been represented?
• What tasks should we include in our next prototype test?

‍

Designer workflow

Review previous findings before writing test tasks. Build studies that extend existing knowledge instead of repeating research your team has already completed.

‍

‍

7. Bring research into the tools you already use

‍

Research is most valuable when it appears alongside the work you're already doing. With MCP, your repository becomes accessible from AI tools that support everyday design work.

‍

Potential workflows

• Generate a design brief from previous research before starting a new feature.
• Draft usability findings directly into Confluence or Notion.
• Format ideas into sticky notes and prep for a design sprint with Miro or Mural.
• Create presentation-ready summaries for design reviews.
• Turn research findings into product requirements for engineering.
• Compare proposed designs against historical usability findings.
• Ask follow-up research questions while designing in Figma with an AI assistant open alongside your work.

‍

Instead of switching between repositories, documents, and reports, research becomes part of your design process.

‍

‍

Designing with confidence

‍

The best design decisions aren't based on intuition alone; they're informed by a deep understanding of user behavior.

‍

MCP makes it easier to bring research into everyday design work, helping you move from evidence to action faster. Whether you're exploring concepts, validating ideas, preparing stakeholder reviews, or planning usability tests, you can use MCP to help your research repository become an active design partner. 

‍

Book a demo or log into your account to get set up with MCP. 

Walk into any product company and you'll hear the same language about being user-centric. So why does the average person still rage-quit apps weekly?  If everyone's truly putting users at the center, something's not adding up. Most companies aren't lying when they say they're user-centric. They genuinely believe it. Leadership believes it. Product teams believe it. The values are probably painted on the office wall. But there's a massive gap between claiming to be user-centric and actually building products that work for users.

‍

‍

‍

Where user insights go to die

‍

‍

Let's trace what actually happens to user research in most organizations. A researcher conducts a brilliant study. They talk to real users, identify genuine pain points, uncover surprising insights about how people actually use the product. The researcher creates a comprehensive report with clear recommendations. They present to stakeholders and then…nothing happens.

‍

Product moves forward based on roadmap commitments made three months ago. Design tackles the projects already scoped. Engineering builds what's already been spec'd and by the time the next planning cycle rolls around, the research is old news. The context has shifted. New priorities have emerged. The research was great. It just never actually changed anything.

‍

The issue here isn't that teams don't value research. It's that the systems aren't set up to close the gap between insight and implementation. Research lives in one place and product decisions happen somewhere else. There's a handoff required. Someone needs to take the insights from the research and translate them into the specific contexts where decisions get made. But who owns that handoff? Usually nobody explicitly. The researcher assumes product managers will incorporate the findings. Product managers assume researchers will flag critical issues. Designers are caught in the middle, trying to advocate for users while also hitting delivery dates. So insights leak at every transition point.

‍

Even when research does make it into decision-making conversations, it's often in the wrong format for action. Researchers are trained to present thorough findings and all of that is valuable. But it's not always actionable. The gap between what users said and what should be done about it, is a lot bigger than most teams realize, but an even bigger problem is that product decisions can’t wait for perfect information. Product decisions need to happen fast, often in real-time, in contexts where pulling up a research report isn't practical. If research insights aren't immediately accessible in the moment decisions are being made, they effectively don't exist.

‍

‍

‍

So…What actually works?

‍

‍

The companies that successfully put users at the center do something different. They build research into the decision-making flow rather than treating it as a separate activity that informs decisions. They make sure that research insights live where decisions happen, integrated into project management tools, design files, and product specs.

‍

Increasingly, they're also using MCP (Model Context Protocol) to connect AI tools like Claude, ChatGPT, and Cursor directly to their research repository – here's how. Instead of searching through reports, teams can ask questions in natural language to surface relevant findings, user insights, and context exactly when they need them, or generate artifacts and workflows grounded in real user research. Insights are accessible in the moment someone needs them.

‍

At Optimal, our design team embeds research findings directly in their Figma files. Not as links to external reports, but as context right next to the designs. When someone's reviewing navigation options, the tree test results are right there. When they're looking at prototype variations, the usability data is embedded in the file. Decisions and insights exist in the same place.

‍

Another great step is to make sure that all research insights are framed as recommendations, not just observations. Instead of "Users struggled with the checkout flow," the insight could be "Simplify checkout to 3 steps instead of 5, based on usability testing where users abandoned at step 4." The researcher isn't just reporting what happened. They're translating it into clear next steps.

‍

We also firmly believe that research should be continuous instead of episodic. Instead of big studies that take weeks and produce comprehensive reports, teams run smaller tests constantly. This doesn't replace deep foundational research. But it supplements it with rapid feedback loops that keep user input flowing into active work.

‍

And lastly - research should be accessible to all. When only dedicated researchers can conduct studies, research becomes a bottleneck. When product managers and designers can run quick tests themselves, with appropriate guidance and quality standards, research becomes embedded in how the team works rather than something teams have to request and wait for.

‍

Being genuinely user-centric requires infrastructure, not just values. You need tools that make it easy to share findings where people actually work. You need workflows that integrate research into product development rather than treating it as a separate track. You need the ability to run research at the speed decisions happen.

‍

Most importantly, you need to make the gap between insight and action as small as possible. The best research in the world doesn't help users if it never influences what gets built.

Imagine you've just joined a new research team. There is a vast amount of research in the repository. Hundreds of interviews, usability tests, surveys, and notes. Everyone tells you, "We've probably researched that already," but nobody knows when or where.

‍

Instead of manually searching projects or asking around, Model Context Protocol (MCP) lets you ask questions about studies conducted in Optimal and instantly surface the evidence you need.

‍

Here are practical ways UX researchers can use MCP with Optimal to understand past research, accelerate new studies, and uncover insights across their repository.

‍

‍

‍

1. Get up to speed on past and current research

‍

‍

One of the best ways to use MCP is understanding what's already known. Instead of combing through different studies, ask MCP to summarize existing knowledge before planning your next study.

‍

Try asking:

• Based on the research I’ve run in Optimal, what are the biggest UX opportunities for our product?
• Summarize the key findings from checkout research over the past year.
• What usability issues have been identified most frequently?
• What research should I read first to understand this project?

‍

‍

‍

2. Define your next research study

‍

‍

Before creating your next study, writing discussion guides or recruiting participants, check what questions have already been answered and which gaps remain.

‍

MCP can help identify opportunities for follow-up research and prevent unnecessary duplication.

‍

Try asking:

• What questions about account creation are still unanswered?
• What themes need further investigation?
• Based on previous studies, what should our next usability test focus on?
• What hypotheses should we validate next?

‍

‍

‍

3. Find supporting evidence faster

‍

‍

Whether you're preparing a presentation or writing a report, MCP can help to surface quotes, observations, participant metadata, and findings in seconds.

‍

Try asking:

• Find participant quotes describing frustration during onboarding.
• Show examples of navigation issues from recent usability tests.
• How many participants completed this study on mobile?
• Which sessions mentioned difficulty finding pricing information?
• Pull task completion rates from all prototype tests in the Dashboard project as a CSV.

‍

‍

‍

4. Discover research before starting from scratch

‍

‍

One of the easiest ways to waste research effort is repeating work that's already been done. Use MCP to explore what's already in your repository before creating a new study.

‍

Try asking:

• What research already exists about navigation?
• Have we previously tested this feature?
• What have we already learned about search?
• Which studies relate to account settings?

‍

‍

‍

5. Identify patterns across multiple studies

‍

‍

The biggest insights often emerge when you zoom out. Instead of reviewing studies individually, MCP can synthesize findings across projects to reveal recurring themes, behaviours, and pain points.

‍

Try asking:

• What pain points appear consistently across checkout studies?
• Compare findings from our last five usability tests.
• What themes have become more common over the past six months?
• Which usability issues keep appearing regardless of product area?

‍

‍

‍

6. Create stakeholder-ready summaries

‍

‍

Research is most valuable when it's easy to share. Use MCP to turn large volumes of research into concise summaries or visualizations tailored to your audience.

‍

Try asking:

• Summarize this quarter's most important customer insights.
• Create an executive summary for leadership.
• Create a pie chart with a breakdown of onboarding studies by study method. 
• What are the three biggest opportunities we should prioritize?
• Write a summary suitable for our product team.

‍

‍

‍

7. Bring research into your existing workflows

‍

‍

Connect it with the AI tools and platforms your team already uses so research becomes part of everyday decision-making.

‍

Examples include:

• Ask research questions and post insights directly into Slack.
• Create a new page in Notion summarizing research findings.
• Draft insight summaries for Google Docs and Confluence.

‍

‍

‍

Best practices for getting the best answers from MCP

‍

‍

Like any AI assistant, the quality of the output depends on the context you provide. A few simple habits can make a big difference.

‍

‍

Start with a clear goal

‍

Rather than asking broad questions, explain what you're trying to achieve. Instead of Tell me about onboarding.

‍

Try: I'm planning a usability study on onboarding. What problems have previous research uncovered that we should investigate further?

‍

‍

Narrow your search when appropriate

‍

Large repositories can contain a wealth of research.

‍

Specify:

• Study tool and/or project
• Research method
• Time period
• Team
• Participant segment

‍

For example: Summarize usability studies about checkout conducted during the past 12 months.

‍

‍

Decide whether you need one study or many

‍

Sometimes you need detailed findings from a single study. Other times you're looking for patterns across dozens of studies. Tell MCP which perspective you want.

‍

‍

Ask follow-up questions

‍

Treat MCP like a research partner rather than a search engine. For example:

‍

• Can you show supporting participant quotes?
• Which studies contributed to this finding?
• Are there conflicting findings?
• What evidence supports this recommendation?

‍

‍

Tell MCP how you want the answer

‍

Different audiences need different outputs.

‍

Ask for:

• Bullet-point summaries
• Executive briefings
• Presentation-ready insights
• Charts
• Tables
• Research reports
• Action items
• Product recommendations

‍

MCP helps researchers spend less time hunting for information and more time generating insights that move products forward. 

‍

How will you use MCP with your Optimal data? Whether you're uncovering past insights, planning new studies, or connecting research with the rest of your tools, we'd love to hear how you're putting it to work.