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, 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
"Historically, researchers spent time moving data between systems, preparing reports, and answering the same questions repeatedly. MCP allows researchers to spend more time on study design, methodology, interpretation, and strategic recommendations."
— James Villacci, Head of Research, Sprig
Four workflows MCP unlocks
1. Advanced quantitative analysis
Use AI workflows to prepare survey data for segmentation, pricing research, MaxDiff, conjoint, and longitudinal analysis without exporting datasets between systems.
2. Cross-study analysis
Analyze dozens of studies simultaneously to identify trends, movement, and recurring patterns across your research program.
3. Research reporting
Generate executive summaries, stakeholder reports, presentations, and recurring business reviews directly from live research data.
4. Custom research agents
Build specialized agents for brand tracking, survey QA, reporting, repository management, or insight generation, all powered by your Sprig data.
Research in an agent-native world
The most interesting use cases won’t be individual prompts. They’ll be workflows.
Researchers will use AI to review studies, analyze findings, generate reports, compare results across projects, and make research more accessible across their organizations. Instead of manually moving data between systems, they’ll build workflows around the research already living in Sprig.
The opportunity isn’t simply connecting Sprig to AI tools. It’s making research available wherever work happens.
That’s why I believe MCP is much bigger than a new integration. Historically, research platforms have been systems of record. As AI becomes the interface for work, they’ll increasingly become systems of context. That’s the future we’re building toward at Sprig.
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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