Your feedback, for every AI agent.

The way product teams work with their tools is shifting fast. A year ago, the default interface to Upvoty was a browser. Today, an increasing share of our power users are talking to Upvoty through Claude, Cursor, or their own LLM-powered agents. Upvoty MCP is a native Model Context Protocol server that makes those workflows first-class, not an after-the-fact integration but a built-in capability of the product.

MCP (Model Context Protocol) is the emerging open standard for how AI agents call external tools. Rather than building a bespoke integration for every AI client, you ship a single MCP server and any compatible agent, Claude Desktop, Cursor, Continue, custom agents built on the OpenAI Agents SDK, can use it natively. The Upvoty MCP server exposes every resource you can reach through our REST API, but in the shape AI models work with best.

What you can actually do with it

The simplest use cases are queries: "show me the top 5 enterprise requests this quarter", "summarize all feedback tagged Mobile from the last 30 days", "which feature requests came from accounts we lost last month". These run in seconds inside your AI client, with no SQL, no dashboard navigation, no copy-paste. Behind the scenes, the agent uses Upvoty MCP to call our REST API, filter by segments, and stream the answer back into your chat.

More advanced workflows go further. Triage incoming feedback automatically by tagging, merging duplicates with Merge AI, and routing to the right team via integrations. Draft replies to specific posts, with the agent pulling context from the entire thread. Identify roadmap themes by clustering posts and recommending what to prioritize. The fact that any of this works without writing custom code is the headline.

Safety, scoping, and audit

Letting an AI agent touch your feedback database is a real security question and we treat it that way. MCP access uses the same scoped tokens as our REST API: read-only for analysis workflows, read-write for automation, admin for full management. Every tool call the agent makes is captured in the same audit log as direct API calls, with the agent identifier, timestamp, and outcome. If something goes sideways, you have a complete record of what the agent did and why.

The shape of the AI-native product team

We think the shape of product teams is shifting toward an AI-native workflow where the agent is the default interface for repetitive tasks, the dashboard is for exploration, and the API is for system-to-system integration. Upvoty MCP is built for that future. If your team uses Claude, Cursor, or any other MCP-aware tool day-to-day, plugging Upvoty in is a 30-second setup that pays back compounding hours every week.

For teams that have not adopted an AI workflow yet, MCP is a low-risk way to start. The setup is reversible (just revoke the token), the scopes are tight (read-only by default), and the audit log gives you full transparency over what the agent actually does. The first time someone on your team types "summarize this week's feedback for me" into Claude and gets a clean answer in five seconds, the value proposition becomes obvious.