PM Copilot
An MCP server that triangulates customer support tickets and feature requests to help PMs decide what to build next.
Real results: Analyzed 2,370 signals (2,136 support tickets + 234 feature requests) across 3 products in 55 seconds. Identified 16 themes, 15 convergent. Top priority: Booking & Scheduling (score: 134.6) — 629 tickets + 77 feature requests pointing at the same problem.
Read the full story: I built an MCP server that changed how I prioritize products — why I built this, how convergent signals work in practice, and what I learned building with Claude Code.
What Makes This Different
- Signal triangulation — Not just data access. Matches support tickets against feature requests to find convergent themes, then scores them with a weighted formula that gives convergent signals a 2x priority boost.
- Composability — Designed to work with other MCP servers. Pass churn data from Metabase or traffic trends from Google Analytics into
generate_product_planviakpi_context, and the methodology adjusts priorities accordingly. - PM methodology built in — Opinionated scoring based on 7 years of real product management across 9 products and 1M+ users. Not a generic framework — an actual decision-making process exposed as an MCP resource.
- PII scrubbing — Customer data never reaches the LLM unfiltered. SSNs, credit cards (Luhn-validated), emails, and phone numbers are redacted before analysis. Agent responses are filtered out of quotes.
