Today we’re launching Sprig MCP.
For decades, research has lived inside dedicated tools. Researchers run studies, analyze results, build reports, and distribute findings across the organization. But as AI becomes part of how teams work, that model starts to break down.
Researchers are analyzing data in Claude. Teams are building workflows in ChatGPT. Organizations are increasingly relying on AI agents to help synthesize information, generate reports, and support decision-making.
The problem isn’t collecting research anymore. It’s getting research into the places where work happens.
That’s what Sprig MCP solves.
Starting today, teams can connect Sprig directly to Claude, ChatGPT, Cursor, Gemini, GitHub Copilot, and other AI tools through the Model Context Protocol (MCP). Instead of exporting CSVs, copying survey responses into prompts, or manually moving data between systems, AI tools can access the customer evidence already living in Sprig.
Research becomes available wherever work is happening, and I believe this is a much bigger shift than a new integration.
Historically, research platforms have acted as destinations. Researchers collected data, analyzed findings, built reports, and then distributed insights throughout the company. As AI becomes the interface for work, research platforms need to become systems of context instead. The value isn’t just storing research. It’s making that research available wherever decisions are being made.
What changes for researchers
"For years, the bottleneck wasn't the research. It was the distribution of it. A study would finish, and the insights would sit in a deck while five teams asked the same questions at standup. MCP changes that. Your work shows up where decisions get made, automatically. The researcher's job shifts from running every step to coaching the system."
— James Villacci, Head of Research, Sprig
Four workflows MCP unlocks
1. Get feedback on your study design before launch
Sprig's Design Agent already runs a bias audit when you build a study. MCP extends that. Pull the study config into Claude or ChatGPT and ask for an independent review from whatever model your team trusts. Useful for non-researchers running their first study who want extra signal before fielding.
2. Pull verbatim quotes for the exec deck
When leadership wants to know what customers think about the new pricing page, you stop digging. The agent queries Sprig themes, filters by date, and returns five quotes mapped to your point. Five minutes, not an afternoon.
3. Compare themes across studies, over time
Most research repositories die the day they're built. MCP makes yours useful. Ask the agent to pull every onboarding study from the last four quarters and tell you what got better, what got worse, and what hasn't moved. Longitudinal pattern recognition without a manual rollup.
4. Send insights to the tools your team already uses
Sprig MCP also works one-way out of Sprig. Drop a study summary into a Notion research repo. Post a thematic digest to #product-research in Slack.
How it works across your stack
Because MCP is an open protocol, Sprig works alongside any other tool your AI client supports. A few combinations worth setting up on day one.
Notion or Confluence. Auto-populate research repos with the latest themes from a relevant study.
Slack. Broadcast theme digests to a research or product channel when a study hits its response threshold.
Google Sheets. Query and clean response data with natural language, no CSV download.
If there's a workflow you're trying to build and the endpoints don't yet support it, write us at support@sprig.com.
See it live
We’re hosting a webinar where James Villacci, our Head of Research, will be walking through real workflow and how to connect it to AI tools like Claude, Codex, Notion, and Slack. If you want to see this end-to-end, save your spot.
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