Great Question MCP launch demo

The Great Question MCP bridges the gap between your research repository where you store and share everything your customers ever said and the tools you're using every day, like Claude, ChatGPT and Cursor.

If you're a researcher, it's your new starting point.

You can pull all of your study data into CLAUDE to analyze studies from one place for PMs, you don't need to wait for a research readout.

You pull the findings yourself in the same place.

You're already drafting PRDs problem statements now cite real users.

User stories come from what participants actually said.

Every claim is traceable back to a real person, a real session and you can get a sense of how issues have changed or sentiment has changed over time.

It's also creating a new jump off point for stakeholders who can now self service access without necessarily needing to go into Great Question itself to understand what we've been learning about customers in the last 30 days, last six months or about a particular topic.

So let's dive in and take a look about how I built a workflow in claude.

I jump into Claude and say, hey, help me write a PID on highlight reels.

We're thinking about rebuilding it anyway.

Bring in data from Great Question Mixpanel, our analytics tool and linear where we post customer support tickets and where all product development happens.

It goes in and pulls from each of these systems and writes together a prd that I can then go and analyze.

Not only to help me understand what the current state of play is from research in our repository, but also putting together more analytics about usage and other issues that have been reported in support tickets, proposing what features we might go and work on.

Let's pull that up now.

Here's the pid.

It's gone and drafted.

it's got a state of where we're currently at from analytics tools, talks about unique users and how many reels they've been creating, then brings in the user research, brings in linear tickets to understand what we already know, writes a problem statement, some goals including how we think it's going to transform and then proposed features we might go and build.

This is great.

This would normally take me, if I wasn't using tools like this, at least a week, maybe a couple of weeks and I probably wouldn't have been anywhere near as thorough as this is.

It's also going against some additional gaps you might need from customer support conversations or maybe some retention data it wants to understand.

It's even gone and taken a technical approach about how we might render the videos, including look at some competitive analysis for different tools, technologies you might go and incorporate.

It's also started thinking about what a mock up might look like and referencing other data we might go and play with.

So how does this actually convert into the product?

Well I've actually gone and put together a mock up from this, taking it out of just being a PID that something static to something I can actually get in front of customers.

Now I haven't yet connected this up to my code base, but that'll be the next step for me to do.

But it's brought in and created a version of what it thinks that a transcribed and reel builder could look like.

It's gone and built a new reel editor which solves some of the problems the customers have talked about, which is they need better ways to control the length of individual clips and they want things like names, titles and names attached to each speaker so you know who's talking.

They also want interstitials on segments, and summaries about what we're trying to communicate so we don't need to listen to every word of the transcript.

It's then gone and built a different way to go and generate these highlight reels whether I want to do them based on particular themes as a study, summary of a particular study based on a particular tag or based on an individual and even give me a prompt to go and generate that and then an easy way to go and share it with analytics to track how people are consuming it all based on that PRD and the input from the Great question mcp.

So as you can see, the Great question MCP makes it easy to bring what we know about customers into our product requirement document, into our product development workflows within Claude and ChatGPT.

In one place, this is what AI native research looks like.

And right now Great Question is the only research platform that works this way.

Our beta customers are using it to create amazing work including research reports, theme analysis, transcript and video analysis and connect more research to tools like Coda, Figma, Magic Patterns and more.

Come and test it out at GreatQuestion MCP.