PM Copilot MCP Server

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_plan via kpi_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.

Architecture

graph TD
    A[Claude Desktop / Code] -->|stdio| B[pm-copilot]
    A -->|stdio| C[Metabase MCP]
    A -->|stdio| D[Google Analytics MCP]
    B -->|Qualitative| E[HelpScout: tickets]
    B -->|Qualitative| F[ProductLift: feature requests]
    C -->|Quantitative| G[Conversion, Churn, Revenue]
    D -->|Acquisition| H[Traffic, Channels, Trends]
    B -.->|kpi_context| A

Claude orchestrates multiple MCP servers. PM Copilot handles qualitative customer signals. Other servers provide quantitative business metrics. The kpi_context parameter is the integration point — no point-to-point integrations required.

Quick Start

git clone https://github.com/dkships/pm-copilot.git
cd pm-copilot
npm install
cp .env.example .env   # Edit with your credentials
npm run build

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "pm-copilot": {
      "command": "node",
      "args": ["/absolute/path/to/pm-copilot/dist/index.js"]
    }
  }
}

Claude Code

claude mcp add pm-copilot -- node /absolute/path/to/pm-copilot/dist/index.js

Or use the .mcp.json already in the project root — Claude Code picks it up automatically.

Tools

`synthesize_feedback`

Cross-references HelpScout tickets and ProductLift feature requests, returns theme-matched analysis with priority scores.

Returns themes sorted by priority score, each with reactive/proactive counts, convergence flag, evidence summaries, and representative customer quotes.

`generate_product_plan`

Builds a prioritized product plan with evidence and customer quotes. Accepts external business metrics via kpi_context.

`get_feature_requests`

Raw ProductLift data access for browsing feature requests directly.