Everyone Says They're User-Centric. So Why Do Most Products Still Suck?

You've invested time, budget, and effort into your research. But when you or someone else needs an answer, finding the right insight often means searching through projects, remembering which study covered the topic, and piecing insights together manually.

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The Model Context Protocol (MCP) changes that. It connects AI tools like Claude, ChatGPT, and Cursor securely to your research in Optimal, so your team can ask questions in plain language and get insightful answers or connect your research to AI-powered workflows.

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Below, we cover what MCP makes possible and how to connect it, so you finally get full value from the research you've already done and the repository you’ve built.

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Getting started: What can MCP do for research teams?

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Once connected, MCP turns your repository into something you can ask directly, so all that past and current research is always instantly accessible. Here are 5 ways to use it:

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1. Research assistance

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Ask "What usability issues have we found recently?" or "What have we learned about onboarding?" Pull participant data and metadata e.g. “How many participants took this study on mobile?”

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2. Discovery

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‍Ask "What research already exists on navigation?" so work isn't duplicated. You can also use MCP to pull quotes or review transcript data. “Surface participant quotes that highlight points of friction when navigating the homepage.”

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3. Executive summaries

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Ask your AI tool to summarize the most important themes from research this quarter or format findings into charts or graphs.

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4. Cross-study synthesis

Surface recurring participant pain points across multiple usability studies at once.

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5. AI assistants & workflows

Connect Optimal research with the tools and workflows your team already integrated with your AI tools, like Zapier, Slack, Jira, and Notion.

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What can you ask? Real questions, by research method

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Here are some practical examples of the kinds of questions teams can ask.
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Across studies

• What themes appear across multiple usability studies?
• What are recurring participant pain points this quarter?
• Which studies were conducted around onboarding in the past year?
• Summarize all checkout-related findings from studies this quarter.
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Interviews

• Can you summarize the key pain points for participants who have downloaded and used the mobile app?
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‍Prototype testing

• Which task had the lowest success rate in the latest prototype test, and what usability issues contributed to it?
• What usability issues contributed to task failure?
• Pull task completion rates from all prototype tests in the Dashboard project as a CSV.

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Tree testing‍

• What % of users found the checkout successfully in last week's tree test, and where did the rest drop off?
• Where did users drop off?

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Card sorting

• Which categories did participants consistently group together in the navigation card sort?

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First-click testing

• Where did users first click when asked to find the Pricing page in the first click test?

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How do you set up MCP with Optimal?

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Step 1: Sign in to your AI tool
Log into your preferred AI assistant (e.g. Claude, ChatGPT, or Cursor).
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Step 2: Connect your Optimal account
Go to your AI tool’s settings and add a new MCP connection. Authenticate your Optimal account via OAuth 2.0 to securely grant access to your Optimal data.
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Step 3: Start asking questions
Return to your AI tool and begin with simple, high-value questions grounded in your research.

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Step 4: Embed it into your workflow‍
Use MCP regularly to explore insights, synthesize findings, and support decision-making.
The most effective MCP implementations are not standalone tools; they are embedded into daily decision-making.

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If your AI tool is already connected to tools like Slack, Jira, Notion, or Zapier, you can use your Optimal research to trigger workflows, such as:

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• Sending Slack alerts when key findings are uncovered
• Creating tickets in Jira when usability issues are detected
• Feeding insights into product documentation tools
• Connecting findings to internal AI assistants used by product and design teams

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You've already done the hard part: running the studies and capturing the findings. The value is sitting in your repository. MCP helps you unlock what's already in your repository, making it easy to discover, reuse, and turn into action.

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Whether a study was conducted yesterday or months ago, you’ll be able to gather insights with MCP to make faster, more informed decisions today.

Research data becomes even more valuable when it can be used alongside the other data your business relies on.

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The Optimal API gives organizations secure, read-only access to their Optimal research data, so you can connect research with the tools and workflows your teams already use. Bring research data into a data warehouse, build dashboards, automate reporting, or combine research results with product analytics and business metrics.

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Instead of manually exporting research data or relying on static reports, you can create a more connected way to access and use your research at scale.

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What is the Optimal API?

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The Optimal API is a secure, read-only way to access research data from Optimal programmatically. It allows your developers, IT or systems administrators, and API or integration managers to connect Optimal with other business systems.

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For example, you can use the API to:

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• Centralize research data in a data warehouse such as Snowflake.
• Build research dashboards in Power BI, Tableau, Looker, or other business intelligence tools.
• Automate reporting so research data can flow into recurring reports and workflows.
• Create an internal research repository that brings research data together in one place.
• Connect research with business data to explore research alongside product analytics, customer data, or business metrics.
• Build custom applications and integrations around your organization's research data.

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Why connect your research data?

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As research programs grow, manually exporting and combining data can become time-consuming. Connecting research data to your existing data infrastructure can make it easier for teams to find, analyze, and share research across the organization.

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For example, a research team could send Optimal data to Snowflake and combine it with product analytics. A research operations team could build a centralized dashboard showing research activity across teams. A data or analytics team could automate recurring reports instead of relying on researchers to export data manually.

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The API helps make research data more accessible beyond the research team.

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What research data can you access?

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The initial release of the Optimal API provides read-only access to:

• Research activities
• Research metadata
• Participants
• Responses and results
• Insights and AI-generated insights

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This means you can retrieve research data from Optimal without changing or deleting it through the API.

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Who can set up the Optimal API?

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Depending on how your organization manages technology and integrations, the person setting up access to the API might be a:

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• Developer
• IT or systems administrator
• API or integration manager
• Data or analytics team member

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You don't need to be the person building the integration to create the API application. The organization admin is responsible for creating the application and securely sharing its credentials with the appropriate person.

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How to keep your API credentials secure

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Your Client Secret works like a password for your API application. Treat it as sensitive information and only give access to people or systems that need it.

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Follow these best practices:

• Never share your Client Secret publicly. Don't send it through Slack, email, direct messages, or other unsecured channels.
• Use a password manager or secrets manager. Store credentials in a secure tool such as 1Password or your organization's approved secrets manager.
• Don't add secrets to source code. Never commit your Client Secret to a Git repository or include it in configuration files, screenshots, or other files that could be shared.
• Limit access to credentials. Only give API credentials to the people or systems that need them.
• Make application ownership clear. Use a descriptive application name so your team can identify what the application is used for and who manages it.
• Delete applications you no longer use. Removing unused applications helps reduce unnecessary access.

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Build a more connected research workflow

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The Optimal API makes it possible to bring research data into the systems your organization already relies on.

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Whether you're building a research repository, connecting research to business intelligence, automating reporting, or combining research with product data, the API can help reduce manual data handling and make research easier to use across your organization.

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The Optimal API is now currently available to multi-team organizations on Enterprise plans.

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.

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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.

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

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1. Get up to speed on past and current research

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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.

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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?

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2. Define your next research study

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

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

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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?

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3. Find supporting evidence faster

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

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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.

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4. Discover research before starting from scratch

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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.

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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?

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5. Identify patterns across multiple studies

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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.

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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?

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6. Create stakeholder-ready summaries

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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.

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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.

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7. Bring research into your existing workflows

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

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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.

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Best practices for getting the best answers from MCP

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

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Start with a clear goal

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

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

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Narrow your search when appropriate

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Large repositories can contain a wealth of research.

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Specify:

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

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

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Decide whether you need one study or many

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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.

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Ask follow-up questions

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

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

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Tell MCP how you want the answer

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Different audiences need different outputs.

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Ask for:

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

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

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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.